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• An efficient method is proposed to update the time-varying dynamics of in-process thin-walled part. • Milling chatter can be predicted accurately based on the updated dynamics of in-process thin-walled part. • The proposed method can greatly improve the updating efficiency of the dynamics of the thin-walled part, compared with the existing methods. • Stability lobe diagram obtained by using the proposed method in simulation is verified by cutting tests. The dynamics of thin-walled parts vary in time with the feed of tool and the removal of material in the milling process, which is a non-negligible phenomenon related to chatter. A new thin-walled parts time-varying dynamics updating method based on a degree of freedom (DOF) reduction model is proposed in this paper for milling chatter prediction. First, to establish the DOF-reduced model, the in-process thin-walled part is divided into two substructures, namely, the substructure with material to be removed and the substructure with finished part, according to the entire removed material. The finite element (FE) model of the substructure with finished part is directly reduced by using the free-interface method since it remains unchanged in the milling process. The FE model of the substructure with material to be removed is reduced by using a combination of the free-interface method and the structural dynamic modification technique. In this way, the changed dynamics of the thin-walled part are updated only in the DOF-reduced model of the substructure with material to be removed. DOF-reduced FE models of the two substructures are coupled by using interface compatibility conditions to establish the DOF-reduced FE model of the entire thin-walled part. Then, the time-varying dynamics of the thin-walled part are obtained by solving the eigenvalue problem of the reduced thin-walled part. Compared to the existing methods, the proposed method updates the time-varying dynamics with high efficiency. Finally, the proposed time-varying dynamics updating method for thin-walled parts is merged into a milling dynamic model to predict chatter. A series of milling experiments are carried out to verify the correctness of the proposed method. The experimental results agree with the simulation results, indicating that the proposed method can be used to predict the time-varying dynamics of the thin-walled parts for chatter analysis. [ABSTRACT FROM AUTHOR]

Planetary gear system is widely used in wind power, construction machinery and helicopters, because of its large transmission ratio, strong load-bearing capacity and small size. Because the gears are usually in high-speed operation, their key components are subject to severe wear and impact damage. When the gearbox is subjected to a failure, it is often difficult to judge the operating status of the gearbox in real time. Since the gearbox system is complex, moreover, it is still difficult for the simulation model to accurately identify the gearbox fault. To address this issue, this paper proposes a digital twin-based signal fusion model to accurately identify gearbox faults at the signal level. The proposed model can identify minor faults occurring in planetary gearboxes by fusing actual and virtual signals. Firstly, on the basis of virtual prototype and the multibody dynamics software, a digital physical model based on Hertz theory and extended finite element method is proposed for gearbox dynamic monitoring and digital simulation, in which the gearbox can be visualized. Secondly, the data-driven method is used to extract and select features from real and simulation signals, and the fault of the planetary gearbox is detected by feature matching. Finally, the real and simulation signals are fused effectively based on variational mode decomposition and phase correction of generalized cross-correlation coefficient. The fault diagnosis result of the gearbox is finally determined by using the envelope spectrum of the fused signal. The experimental results show that the virtual real fusion method effectively improves the performance of the traditional signal diagnosis methods, and the fusion of the actual and virtual signals strengthens the fault features of the fused signals effectively. [ABSTRACT FROM AUTHOR]

Industry 5.0 advocates for a sustainable industry, particularly in terms of energy. One of the most fundamental elements of comprehension is knowing when the systems are operated, as this is key to locating energy intensive subsystems and operations. Such knowledge is often lacking in practice. Activation statuses can be recovered from sensor data though. Some non-intrusive sensors (accelerometers, current sensors, etc.) acquire mixed signals containing information about multiple actuators at once. This paper considers different industrial settings in which the identification of binary sub-system activation sequences is sought. In this context, source signals are assumed to be quasi-stationary and they may be correlated. Existing methods either restrict these assumptions, e.g. , by imposing orthogonality or noise characteristics, or lift them using additional assumptions, typically using nonlinear transforms. This paper addresses these limitations, and introduces the unsupervised complex semi-binary matrix factorization (ℂ SBMF) as its main contribution. In particular, we show that the exact recovery of source activation sequences from non-intrusive sensor data is intrinsically tied to the presence of problematic phase shifts, the causes of which are detailed. A greedy algorithm is proposed, iteratively resynchronizing sources to converge towards the maximum decomposition of each operation despite these phase shifts. The ℂ SBMF is verified and compared to existing techniques on synthetic use cases, then validated on experimental data with signals of different nature. To that occasion, the CAFFEINE dataset for unsupervised time series multi-label classification is introduced. [ABSTRACT FROM AUTHOR]

It is usually assumed that faulty bearings produce second-order cyclostationary (CS2) signals, and thus the natural process for their diagnostic analysis involves first the removal of first-order cyclostationary (CS1) components, such as from gears, followed by amplitude demodulation of an 'informative' frequency band, and subsequent envelope analysis, in which the spectrum of the (squared) envelope is inspected for signs of a fault, typically manifesting as discrete frequency components at or near one of the expected fault frequencies, the latter calculated from basic kinematic relationships. However, recent theoretical research, supported by empirical evidence from the aero industry, has shown that bearing signals are in fact not purely CS2, but rather exhibit both CS1 and CS2 properties, dominant in the lower and higher frequency ranges, respectively, with the 'crossover' between these ranges proportional to machine speed. Thus, for high-speed bearings, where the frequency range available for analysis may span only a handful of fault frequency harmonics, the relative importance of CS1 signal content is far greater than for lower speed applications, and it can no longer be assumed that the envelope spectrum is the optimal diagnostic tool. These and other issues associated with high-speed bearing diagnostics are discussed in this paper, using as an example the aircraft engine data provided by Safran for the diagnostic contest at the 2015 Surveillance 8 Conference in Roanne, France. The objective of the paper is to guide the reader in the analysis of high-speed bearing signals, pointing out in particular the ways in which the required analysis techniques differ from the 'conventional wisdom' of the diagnostics field, most of which is based on more common, lower speed applications. [ABSTRACT FROM AUTHOR]

• The effect of nonlinearity on the performance of Basis Pursuit (BP) is examined. • It is shown the performance impairment from nonlinearity can be notably mitigated by using the BP a set selector. • It is shown that normalization of the Jacobian to equal column norm leads to notable improvement when the likelihood of damage is uniform in the parameter space. The Basis Pursuit (BP) is an optimization statement used to search for the sparsest solution of underconstrained problems of the form Φ x = b using ℓ 1 minimization. This paper shows that approximating nonlinear problems as linear can result in considerable compromise in the capacity of the BP to attain the minimum cardinality solution and that this impairment can be mitigated by treating the strategy as a selector of a set of unknowns that renders the problem fully constrained, subsequently solving with due consideration for the nonlinearity. It is shown that gains realized by the selector approach derive primarily from the fact that the part of the nonlinearity that projects onto the right-hand side, b , has no effect on the relative values of the solution, x , and that the orthogonal residual seldom modifies the largest entries. The paper considers damage characterization as the application domain and shows that normalization of the Jacobian to equal column norm, a step that is standard when BP is applied in most fields, but not implemented so far in structural engineering, leads to large improvements when the probability of damage across the parameter space is essentially uniform. [ABSTRACT FROM AUTHOR]

[Display omitted] Thin walled and high rib components (TWHRC) are widely used for rocket cabins, aircraft wings, panels and frames in aerospace equipment as critical load bearing structures. This paper is aimed to develop a novel multi-DOF envelope forming machine (MEFM) with parallel kinematic mechanism (PKM) to realize efficient manufacturing of TWHRC with high performance. Since the heavy load MEFM with PKM has many total differences with the light load PKM, such as dynamic error and precision control, this paper conducts research on dynamic error prediction modelling and precision control method for this novel heavy load MEFM. Firstly, the configuration of MEFM is developed and the kinematic model of MEFM is established. Then, a RFC (rigid-flexible coupling) and RFE (rigid finite element) coupling dynamic model of MEFM is established considering time-variation of kinematic configuration caused by large deformation of all the kinematic elements. Based on the dynamic model, the dynamic deformation and error of MEFM are predicted and their generation mechanisms are revealed. It is found that the linear compression deformation of two-force links is the largest among all the kinematic elements, and it is the most sensitive to the heavy forming load and the most insensitive to the installation error of strain gauges, thus suiting for the feedback of large deformation of MEFM. Based on the above findings, a novel link strain feedback and dynamic error compensation control method is proposed. Finally, experiments under different control methods are conducted, showing that the motion precision of envelope tool and forming precision of formed components under the proposed control method are remarkably improved compared with that under the traditional control method (The geometric deviation of formed components under the proposed control method is remarkably reduced to −18 ∼ 32um from −64 ∼ 54um under the traditional control method). The experimental results verify that the proposed dynamic error prediction model and link strain feedback control method for heavy load MEFM are reasonable. [ABSTRACT FROM AUTHOR]

Rank-based statistics offer an appealing framework to non-parametric robust trend detection. Especially, the Mann–Kendall test detects the presence of a trend in a series. This paper investigates the benefits of using the Mann–Kendall test, which seems to have remained largely unnoticed in the context of vibration-based condition monitoring. Two contributions structure the present paper. First, theoretical foundations of rank-statistics are reminded and the Z-score is introduced, a statistical metric measuring the presence of a trend in a given series. The performance of the Mann–Kendall test is investigated and confronted to different numerical cases involving series of increasing condition indicators. Second, based on this understanding, the Z-score metric is exploited to design two trend-oriented signal processing tools dedicated to vibration-based condition monitoring. Namely, a new tool named the Mannagram is introduced as a dyadic filterbank representation of the trend of indicators series. It shows effective in the informative band selection problem and unveils more interpretable indicators. Besides, a concise representation of trend by frequency bin is introduced and coined the Kendrum. This representation is shown to help the diagnosis by summarising the trend information of series of spectra. Both methods are demonstrated on run-to-failure series of vibration signals from industrial wind turbines. • The Mann–Kendall test for the trend analysis is presented. • The Z-score as a trend scalar indicator is highlighted. • The Mannagram , a trend-based signal processing tool for informative frequency band selection is introduced. • The Kendrum , a trend-based signal processing tool for spectral analysis is introduced. [ABSTRACT FROM AUTHOR]

In this paper, an improved LuGre model was established based on the micro-convex assumption, Hertz contact theory, and thermal conditions. The displacement-tangential force and velocity-tangential force hysteresis curves under different temperature conditions were obtained by the dry friction testing experiment. Further, this paper constructed an objective function for the proposed friction model, identified the parameters through the experimental data based on an improved quantum genetic algorithm, and verified the effectiveness and superiority of the model identification results. Finally, this paper analyzed the matching degree between the model identification results and experimental data, confirmed the accuracy of parameter identification results with temperature changes, and put out the specific parameters of the improved model in different thermal environments. This study can provide theoretical guidance for accurately characterizing the friction characteristics of contact interfaces under different temperature conditions. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel mode coupling mechanism applicable to robotic milling is developed. • The feedback between dynamic milling forces and vibrations is modeled. • The low-frequency chatter is efficiently predicted. The low-frequency chatter (LFC) of the robot body during milling severely constrains the machining efficiency. Taking the classical mode coupling mechanism as the underlying foundation, previous studies have developed various stability models to predict the LFC in robotic milling. However, the classical mode coupling mechanism has recently been proven to be applicable only to operations such as threading and boring, showing significant deviations in robotic milling. In response, this paper proposes a novel vibration feedback-based mode coupling (VF-MC) mechanism applicable to robotic milling based on robotic modal directionality, which describes the coupling and feedback process of modal vibration among multiple modes through dynamic cutting forces. The feedback process is modeled considering the time delay effect. The LFC can be judged by the trend of the amplitudes of the feedback vibrations. The effectiveness and efficiency of the VF-MC model for predicting the LFC are verified by comparing with the milling experiment results and the traditional mode coupling chatter model. Furthermore, the dynamical conditions for the LFC are investigated based on the VF-MC mechanism with the experimental evidence. The core contribution of this paper is to quantitatively reveal the feedback mechanism of low-frequency self-excited vibration among modes in robotic milling, and further develop the VF-MC model to efficiently predict the LFC. The code for the VF-MC model is available at https://github.com/Dr-JwWu/VF-MC__A-chatter-model-for-robotic-milling. [ABSTRACT FROM AUTHOR]

This paper introduces a novel machine learning-based optimization strategy for multi-functional acoustic black hole (ABH) metaplates. The primary objective is to achieve a multi-functional metaplate with excellent performance in elastic wave attenuation and load-bearing capacity simultaneously. The paper begins by describing the design of nanocomposite ABH metaplates, presenting a new pathway to realize multi-functional metaplates. Then, a semi-analytical method, based on plate theory and the Bloch–Floquet theorem, is introduced to consider the band structure of the nanocomposite metaplates. Through systematic analysis, the impacts of the ABH effect, nanocomposite reinforcements, and the viscoelastic damping layer on the bandgaps and strain energy compliance are highlighted. Meanwhile, two optimization objectives representing bandgap characteristics and in-plane stiffness are derived respectively. Subsequently, a deep learning surrogate model is employed to establish a relationship involving significant parameters with the optimization objectives. The performance evaluation confirms accuracy and computational speed of the surrogate model. Finally, an optimization strategy based on deep reinforcement learning is proposed to obtain multi-functional metaplates with superior bandgaps, enhanced in-plane stiffness, or both. The robustness and efficiency of the strategy are demonstrated under different tests. The results show that the proposed strategy can achieve identical results as the genetic algorithm and nondominated sorting genetic algorithm-II, while surpassing them in computational efficiency and balancing multiple objectives. The findings of this study serve as valuable references for the future development and application of multi-functional advanced metamaterials. [ABSTRACT FROM AUTHOR]

Dispersion diagrams play a crucial role in examining, analyzing and designing wave propagation in periodic structures. Despite their ubiquity and current research interest, introductory papers and reference scripting tailored to novel researchers in the field are lacking. This paper aims to address this gap, by presenting a comprehensive educational resource for researchers starting in the field of periodic structures and more specifically on the study of dispersion relations captured by dispersion surfaces or curves in dispersion diagrams. The objective is twofold. A first objective is to give a detailed explanation of dispersion diagrams, with graphical illustrations. Secondly, a documented Matlab code is provided to compute dispersion curves of 3D structures with 2D periodicity using the so-called inverse approach. These dispersion curves are obtained with numerical simulations using the finite element method. The code is written for elastic wave propagation and orthogonal periodicity directions, but can be extended to other types of linear wave propagation, non-orthogonal periodicity directions or 1D and 3D periodicity. The aim of this code is to serve as a starting point for novice researchers in the field, to facilitate their understanding of different aspects of dispersion diagrams and serve as a stepping stone in their future research. • Educational paper targeting researchers starting in the field of periodic structures. • Introduction to derive and interpret dispersion curves. • Focus on numerical dispersion curve calculations with the FE method. • Implementation provided for calculation of 2D periodic dispersion curves in Matlab. [ABSTRACT FROM AUTHOR]

The inverse synthetic aperture Ladar (ISAL) is an important system for observation of space objects. Here, a 2-D ISAL imaging and tracking algorithm is proposed for low observable maneuvering extended objects tracking (M−EOT) such as small space UAVs. When imaging maneuvering objects with ISAL, dispersion and Doppler frequency time-variation exist in the range and cross-range echo signal, respectively. Additionally, the ISAL system can achieve high-resolution images for long-range moving objects, while its performance is affected by atmospheric turbulence. Atmospheric turbulence is a very complicated random state formed by the instability of regular laminar flow at a high maneuvering velocity, which is caused by the random motion of the atmosphere and lead to the random fluctuation of the atmospheric refractive index. Therefore, in real-time M−EOT scenario, the measurements distribution over the whole object is not symmetrical but distributed and skewed in some portions while a target maneuvers due to the unbalanced of reflection that caused by the atmospheric turbulence. In practice, the micro Doppler effect caused by micro moving components such as drone rotors will seriously affect the focused ISAL imaging of the M−EOT's rigid body. To solve these problems in real-time low observable M−EOTs imaging and tracking from 2-D ISAL system, a novel robustness measurement model and recursion multi-Bernoulli track-before-detect filter based on a multiple model skewed non-symmetrically normal distribution is represented with more than one ellipse and lower complexity. In this paper, the Radon-Laplace method is presented to reduce this micro-Doppler effect in ISAL imaging. Additionally, we will eliminate the effect of atmospheric turbulence in ISAL Imaging by using the network-based variance–covariance estimation (NVCE) weighting method. In particular, we describe the M−EOT's state through the random matrices model (RMM). In RMM, the extent is applied by one ellipse, which often lose information about size, heading, and shape, when the scatter centers are distributed symmetrically around the M−EOT's centroid. In this paper, a new observation model used to modify a sub-RMM by considering the characteristics of 2-D ISAL data is proposed. Simulation results demonstrates the proposed robustness algorithm with the influence of the atmospheric turbulence and micro-Doppler. [ABSTRACT FROM AUTHOR]

Digital technologies can be used to gather accurate information about the behavior of structural components for improving systems design, as well as for enabling advanced Structural Health Monitoring (SHM) strategies. New avenues for achieving automated and continuous structural assessment are opened up via development of virtualization approaches delivering so-called Digital Twins (DTs), i.e., digital mirrored representations of physical systems relying on fusion of simulation models and real-time monitoring data. In this framework, the main motivation of the work presented in this paper stems from the existing challenges in the implementation and deployment of a real-time predictive framework for virtualization of dynamic systems. Kalman-based filters are usually employed in this context to address the task of joint input-state prediction in structural dynamics. A Gaussian Process Latent Force Model (GPLFM) approach is exploited in this work to construct flexible data-driven a priori models for the unknown inputs, which are then coupled with a combined deterministic-stochastic state-space model of the structural component under study for Kalman-based input-state estimation. The use of Gaussian Process (GP) regression for this task overcomes the limitations of the conventionally adopted random-walk model, thus limiting the necessity of offline user-dependent calibration of this type of data assimilation methods. This paper proposes the use of alternative covariance functions for GP regression in structural dynamics. A theoretical analysis of the GPLFMs linked to the investigated covariance functions is offered. The outcome of this study provides insights into the applicability of each covariance type for GP-based input-state estimation. The proposed framework is validated via an illustrative simulated example, namely a 3 Degrees of Freedom (DOF) system subjected to an array of different loading scenarios. Additionally, the performance of the method is experimentally assessed on the task of joint input-state estimation during testing of a 3D-printed scaled Wind Turbine (WT) blade. [ABSTRACT FROM AUTHOR]

• A dynamic model for coupled unit-plant structure of PSPS is constructed. • The transient process of HGS is integrated into the unit and plant structure. • The kinetic properties of coupled unit-plant structure are scrutinized. • The grounds behind the unsteady state oscillations on structures are explored. • Suppression laws of MRD on vibration in coupled unit-plant structure are elucidated. The versatile regulatory capacity of pumped storage power station (PSPS) stems from the reversible pumped turbine unit switching between multiple operating conditions. Nevertheless, the unit and plant structure experience heightened vibrations owing to the intricate interplay of hydraulic, electrical, and mechanical excitations during transition processes, and the vibration responses fluctuate as operating conditions shift, posing a significant challenge to the safety and high quality operation of power station. To tackle the practical engineering issue, this paper delves into the sudden load-up scenario in power generation situation of pumped turbine, leveraging a finite element model encompassing the coupled hydraulic-mechanical-electrical-structural system (HMES) of PSPS and a mathematical model of unit shaft system, the impacts of dynamic loads induced by various vibration source on the unit and plant structure during transient condition, are meticulously analyzed and discussed through numerical simulations. Additionally, the magnetorheological damper (MRD), is incorporated into the PSPS to explore the effectiveness of MRD in controlling transient vibration in the interconnected unit-plant structure. The results indicate that the system parameters and associated load excitations undergo rapid fluctuations during the load-up process, bringing about noticeable oscillations in vibration amplitudes of rotor and turbine. The floors and fan hood of plant are the most vulnerable areas in terms of structural strength, with associated displacement amplitudes being most noticeable. The incorporation of MRD proves to be highly effective in mitigating the unsteady vibrations in both the unit and the plant structure during load-up course, as well as optimizing the motion patterns of rotor and turbine, promoting a notable reduction in frequency amplitudes of spectrum. Moreover, the maximal transient displacement amplitude of unit is significantly diminished, with the rotor experiencing a decrease of approximately 79%, while the amplitude of turbine declines by 16%. The displacement fluctuation ranges of the plant structure are observably diminished, and the complex frequency components of vibration signal are efficiently controlled. The transient vibration characteristics of the coupled unit-plant structure and the damping behavior of MRD during the transition process are unveiled by the research findings in this paper, which can offer valuable insights for the stability assessment and the research on vibration management of HMES for PSPS under the unsteady operation conditions. [ABSTRACT FROM AUTHOR]

Seismic fragility analysis and seismic resilience assessment are pivotal tasks within the performance-based earthquake engineering framework. The former aims to quantitatively assess the probability of engineering structures sustaining various degrees of damage when subjected to earthquakes of varying intensity levels. In contrast to many traditional seismic fragility methods, this paper introduces a precise and versatile approach grounded in reliability analysis, with the goal of eliminating the restrictive lognormal assumption. Initially, the proposed method involves conducting seismic reliability analysis of structures using the Fractional Exponential Moments-based Maximum Entropy Method (FEM-MEM). This approach efficiently yields failure probabilities. Subsequently, an Adaptive Gaussian Mixture Model (AGMM) is presented for fitting a set of failure probability points, producing comprehensive and continuous seismic fragility curves. Notably, AGMM offers remarkable flexibility in modeling fragility curves, ensuring precise estimations for subsequent resilience assessments. Building upon the results of the fragility analysis, the paper proceeds to assess the seismic resilience of structures. The proposed method's feasibility and effectiveness are validated through two illustrative numerical examples. The results demonstrate that, in comparison to the conventional lognormal assumption, the proposed method offers superior accuracy and broader applicability for conducting seismic fragility analyses of structures. • A flexible semi-parametric model is proposed for accurately fitting fragility curves. • An adaptive strategy is developed to update the model. • The proposed method can avoid the conventional lognormal assumption. • The proposed method is validated through numerical examples. [ABSTRACT FROM AUTHOR]

Superconducting pinning maglev train, with the advantages of heavy self-stable levitation and super-low resistance, is expected to become a high-speed transit in the future. With the increase in speed, the more will the irregularity of the PMG (permanent magnet guideway) affects the safety and comfort of the superconducting pinning maglev train. This paper is based on the PMG of the test line for the first high-speed superconducting pinning maglev engineering prototype train in China, to measure and study the magnetic irregularity of the PMG and its characteristics. Experiments were designed to measure the irregularity with a single Dewar maglev system, which can obtain the equivalent geometric irregularity directly acting on the maglev unit of the vehicle. Firstly, the frequency response function is developed based on previous studies, which can transform the response signal of the Dewar into the irregularity signal of the PMG. Secondly, the velocity and position are measured by image recognition of ground markers. Then, the dynamic signal of the Dewar maglev system moving along the PMG is gained and conversed into equivalent geometric irregularities by the frequency response function. Finally, an equivalent geometric irregularity PSD (power spectral density) function is formed, and it can generate the geometrically random track irregularity for vehicle dynamic simulations. The dynamic simulation result suggests that the superconducting pinning maglev train has the potential for smooth operation at high-speed under the excitation of the current PMG. The studies of this paper can help to understand the irregularity of the PMG further, and the gained PSD function can provide a general reference for the dynamic study of the superconducting pinning maglev train. [ABSTRACT FROM AUTHOR]

Employed extensively in industry, planetary gears offer a high power density and high efficiency. However, their operation in frequently dynamic conditions does accelerate the risk of gear failures. This paper explores the phenomenon of fatigue cracking in planetary gearsets, more specifically in thin-rim helicopter main rotor transmission planet gears. Such fatigue cracks typically initiate at or near the planet bearing outer raceway surface and can propagate through the gear body. Whilst such a failure is catastrophic for any gearbox, it is especially disastrous in the case of a helicopter gearbox due to the potential loss of life involved. Given that the reliable detection of planetary gear cracks remains a significant challenge for the vast majority of condition monitoring approaches, a data contest was organized by DSTG at the HUMS2023 conference. DSTG conducted a unique test campaign where vibrations were measured on a main rotor gearbox of a Bell Kiowa 206B-1 (OH-58) helicopter. This campaign produced an extensive vibration data set that was partially shared with the contestants. The purpose of the contest entailed the early detection of the rim gear crack as well as tracking the fault progression. This paper presents the main observations of this data challenge and goes into more detail with regard to the employed approaches as well as the main difficulties encountered when analysing planetary gear vibration data. • Fatigue cracking in planetary gearsets is investigated. • An extensive description of the HUMS2023 data is provided. • The results of the reference benchmark approach are showcased. • The winning methodology is demonstrated on the full public dataset. [ABSTRACT FROM AUTHOR]

Crane swing & relevant vibration are critical safety and efficiency issues to be addressed in construction automation, involving various disturbance and uncertainties. This paper presents for the first time a controller designed for 5-DOF tower crane systems with no velocity signals, and its effectiveness is experimentally verified. Compared to previous controllers, the new design utilizes an adaptive output feedback control method with accurate online gravity compensation, which solves the problems of unavailability velocity, noise amplification by numerical difference, and steady state error of cable in tower crane control. Additionally, the saturated control inputs are guaranteed by employing bounded functions. The results of this study unveil that the controller proposed in this paper can simultaneously achieve accurate trolley/jib/cable positioning, rapid payload swing suppression and elimination, as well as precise payload mass estimation, and guarantee control inputs constraints. This study provides a new approach for accurate control of tower crane systems. [ABSTRACT FROM AUTHOR]

Semi-active suspensions are important components for improving vehicle ride comfort. In this paper, a new road unevenness classification method that is based on tread band vibrations is proposed. Moreover, this method is implemented in the control algorithm of a semi-active suspension with a magnetorheological damper (MRD). The core contributions of this paper include establishing the simulation environment for the tread band vibration based on the rigid-flexible coupling ring tire model and the hardware testing platform based on an intelligent tire and a 1/4 vehicle vibration bench. In this testing platform, an intelligent tire sensor is installed in the tire to reflect the tread band vibration. The comparison of different machine learning algorithms and measured accelerations indicates the effectiveness of using the decision tree algorithm and the tread band vibration for road classification. Furthermore, a hardware architecture is proposed in this paper for real-time communication between wirelessly transmitted intelligent tire acceleration signals and a suspension controller, achieving online classification and adaptive adjustment of the optimal control parameters for the semi-active suspension. In summary, intelligent tires and suspension control systems are successfully integrated in this study, providing the possibility and basis for developing more complex general chassis control systems that include intelligent tires. [ABSTRACT FROM AUTHOR]

Analysis of structural vibrations presents challenges due to the coupling between different degrees of freedom, often preventing the derivation of analytical expressions for quantities such as poles, vibration amplitude, etc. While numerical methods are commonly employed to tackle such challenges, their purely numerical nature prevents achieving a comprehensive understanding of the system behaviour. This paper builds on a novel recursive transfer function formulation recently proposed by the authors to analytically characterise the dynamics of generic undamped mass-chain systems. This approach provides fundamental properties and analytical insights that cannot be obtained through current numerical approaches, representing a significant breakthrough in the understanding of vibrations and offering valuable insights for analysing complex vibrating systems. These results presented in this paper allow for a rapid assessment of the minimum phase nature of the vibration system through straightforward stability assessment, and present analytical expressions for sums and multiplications of natural frequencies squared by induction and Vieta's formulas. Furthermore, it derives conditions for existence of repeated roots using direct analytical derivation of the characteristics equations, including purely imaginary roots and repeated roots at the origin, showing that repeated roots can only occur when some forms of spatial symmetries exist, and confirms bounds for the arithmetic mean of natural frequencies by using inequalities between geometric, arithmetic and quadratic means. Additionally, analytical conditions are established for systems with different degrees of freedom to share the same roots, providing a theoretical foundation for eigenvalue assignment in the time domain or pole placement in the frequency domain. This foundation enables the determination of desired natural frequencies from scratch using transparent analytical conditions, without relying on complex algorithms typically required by current inverse methodologies involving passive structural modifications or active controllers. Moreover, this paper also explores the relationship between independent and coupled vibrations, providing insights for comparing natural frequencies. Overall, this paper contributes to a deeper understanding of structural vibrations through analytical exploration and offers practical guidance for eigenvalue assignment and natural frequency analysis. [ABSTRACT FROM AUTHOR]

• A transformation matrix T is used as a correlation technique. • Matrix T is used to know the source of discrepancies between two models. • Matrix T is factorized with the polar and QR decompositions. • Matrix T is a rotation matrix in the case of no mass discrepancies. • A novel version of the modal assurance criterion (ROTMAC) is proposed. According to the structural dynamic modification theory, the perturbed mode shapes can be expressed as a linear combination of the unperturbed mode shapes through a transformation matrix T. This matrix is proposed in this paper as a powerful technique to determine whether the discrepancies between two models can be attributed to differences in stiffness, in mass, or both. It is demonstrated that matrix T becomes a rotation matrix when there are no mass discrepancies. In the case of mass discrepancies, or a combination of mass and stiffness differences, matrix T can be decomposed into a product of a rotation matrix and a matrix containing information about the changes in scaling and in shear. The angle of rotation depends on the closeness of the modes, and large rotations can be obtained when the system presents closely spaced or repeated modes. The polar and the QR decompositions are used in this paper to factorize matrix T as a product of two matrices, one of them being a rotation matrix. A new version of the modal assurance criterion (MAC), denoted in this paper as rotated MAC or ROTMAC, is proposed to detect mass discrepancies between two models. The equations and the conclusions derived in this paper have been validated through numerical simulations on a 2-degrees-of-freedom system and by correlating a numerical model and an experimental model of a square laminated glass plate. [ABSTRACT FROM AUTHOR]

This paper presents a theoretical study on the implementation of a self-tuning vibration absorber for tonal vibration control. The study considers a tuneable vibration absorber, composed by a spring-mass-damper system with variable stiffness and damping components, which is attached to a single degree of freedom mechanical system subjected to a harmonic excitation. The online tuning of the absorber stiffness and damping parameters is performed using an extremum seeking algorithm set to minimise the vibration kinetic energy of the hosting system. The paper proposes a two-paths tuning approach, which, starting from initial estimates of the stiffness and damping parameters of the absorber, searches for their optimal values along constant-damping and constant-stiffness paths respectively. The study presents a comprehensive analysis on the implementation of the algorithm at off-resonance and at resonance frequencies of the hosting system. Also, it investigates how the intrinsic parameters of the algorithm influence the convergence to the optimal stiffness and damping values. The proposed tuning methodology does not require a reference signal for the tonal excitation and, thus, can be suitably implemented both for fixed and time-varying tonal disturbances. [ABSTRACT FROM AUTHOR]

Offshore wind power is rapidly becoming an essential component of the transition to clean energy. As turbine design capacities continue to increase, there is a growing interest in monitoring both individual turbines and entire wind farms to ensure their performance while also reducing the levelized cost of energy. However, obtaining reliable and comprehensive data on these structures can be challenging, as it often requires costly and potentially dangerous installation procedures and significant computational resources. Therefore, it is critical to predict the needed information to properly assess the performance of offshore wind turbines when not available. To address these challenges, this paper introduces a modified Hidden Markov Model (HMM) framework-based strategy and a companion Python library, Hela. The proposed HMM-based framework incorporates a "smart" initialization strategy and regularization to overcome some of the limitations of applying HMMs to experimental cases. This Python library introduced in this paper is a highly flexible and customizable HMM library for training and inference. This work is all carried out within the use-drive context of offshore wind. [ABSTRACT FROM AUTHOR]

• A damage localization and quantification method without damage labels is proposed. • An efficient method to quantify bridge damage severity level is proposed. • An efficient method for generating damaged structure dynamic responses is proposed. Among the existing unsupervised bridge damage detection methods, it is difficult to quantify bridge damage severity levels, although some of the methods can achieve the localization of bridge damage. In the existing research, the damage index (DI) curve is calculated by the monitoring point dynamic response (inclination or deflection) of the bridge in the damage state, and the localization of the bridge damage can be achieved by the DI curve without the need for damage labels. Based on this bridge damage localization method, this paper proposes an efficient bridge damage severity level quantification method. Since the different DI curves calculated under the same damaged state basically coincide with each other, the DI curves will change with the bridge-damaged state. Therefore, the quantification of the bridge damage severity level is achieved by only calculating DI curves of different damage degrees at the damage location. To efficiently calculate the DI curve, this paper proposes a method for generating the monitoring point dynamic response of the specified damage bridge. By the specified damage bridge finite element model (FEM) static simulation to obtain arbitrary vertical nodal loads and monitoring point responses of the relationship function, and then combined with the healthy state of the bridge monitoring point dynamic response can be generated monitoring point dynamic response of the specified damage bridge. Since the method in this paper is based on real measured data to calculate the DI curve, it is more robust to noise and modeling errors than the FEM dynamic simulation method, and the modeling of this method only needs to update the model stiffness parameter to reduce the modeling cost. In the example of this paper, the method of this paper calculates the DI curve of one damage state only needs about 7 s about 437 times the computational efficiency of the FEM dynamic simulation method, which can achieve the fast quantification of bridge damage severity levels. Numerical simulations demonstrate that the method can localize and quantify multi-damage in bridges under unknown loads, a small number of sensors, noise, and modeling errors. However, the method in this paper still has limitations that do not apply to the localization and quantification of bridge pier damage and nonlinear models. [ABSTRACT FROM AUTHOR]

In this paper we investigate the nonlinear dynamic behavior of a rotor with an architecture targeting space turbopumps. Depending on the rotor speed of rotation, these pumps can be supported by ball bearings with a loose fit, i.e. having an outer-race housing clearance, and by annular seals providing radial fluid forces thanks to the Lomakin effect. Moreover, such turbopumps often operate in supercritical regime, which means that they must pass through critical speeds while undergoing the lowest vibration levels to ensure pump safety. In this context, the aim of the paper is to analyze the case of an academic rotor test bench designed to present a dynamic bending behavior similar to that of space turbopumps. The investigation focuses on the test bench equipped with two hydrodynamic bearings and two ball bearings with loose fit. The rotor response when passing through its first critical speed and triggering nonlinear bearing-housing contact phenomena is assessed experimentally and numerically. The hydrodynamic bearings are located at the two shaft ends and the ball bearings at the middle of the shaft. The rotor finite element model is detailed first and the test bench presented. Then, the nonlinear dynamic response of the rotor subject to a mass unbalance and a permanent bow is obtained. The numerical results are computed using either a full transient step-by-step time integration technique or an arc-length continuation method. The main objective is to assess how the nonlinearity stemming from the loose fit of the ball bearings alters the original rotordynamics. Moreover, a parametric analysis based on mass unbalance and on outer-race housing clearance is conducted. Major nonlinear phenomena are highlighted such as the resonance peak flattening at the critical speed, and the numerous harmonics triggered by the hydrodynamic bearing and contact forces. The results obtained and the model proposed here are of considerable relevance for the design of space turbopumps and other pumps having a similar architecture. [ABSTRACT FROM AUTHOR]

Adaptive Cruise Control (ACC) is a multi-objective control problem, where the balance of the ride comfort and safety performance of vehicles is a challenge. This paper proposes a multi-objective dynamic coordinated ACC based on variable weight model predictive control (MPC). The weight coefficients are regulated according to the inter distance and relative speed between the front vehicle and ego vehicle through a fuzzy controller. To improve the object detection accuracy, the decision-level sensors fusion scheme based on millimeter wave radar and camera is designed. The upper and lower layers of the hierarchical ACC controller are implemented. The proposed ACC control system is tested on a smart electric vehicle. The dynamic coordinated ACC can increase the tracking accuracy of the safe inter distance to enhance the vehicle safety in scenarios where there is possible risk of collision, such as when the inter distance is small and the relative speed is negative. In some safe conditions, the dynamic coordinated ACC can reduce the acceleration and jerk to improve the ride comfort. The performance of driving safety and ride comfort are dynamically coordinated and adapted to the changing driving conditions through the implementation of the dynamic ACC. The ACC has the ability to adapt to these changes and respond accordingly. • A dynamic coordination idea is proposed to regulate the weight factors of MPC. • The decision-level sensors fusion scheme is developed to improve detection accuracy. • The proposed MPC-based ACC system is tested on a real vehicle. • The experiment in this paper promotes the development of MPC in real vehicles. [ABSTRACT FROM AUTHOR]

This paper presents a virtual sensor scheme for vibroacoustic radiation applications based on experimental modal state-space models and sparse measurements. A Kalman filter is used for the estimation combining data from the acoustical and the structural domain together with the numerical modal process model. It is proposed to use Gramians, as an energy-related measure of a system's observability which enables assessing the observability of the coupled vibroacoustic system in the modal state-space domain and the physical state domain counterpart. This Gramian-based observability metric reveals modes or states of high observability, which ensure good estimation results in the subsequent estimation. The proposed method is validated using an industrial test setup for which, due to its high complexity, modelling with first-principle techniques is not feasible. The estimation results demonstrate improved performance compared to a pure forward use of the identified model, indicating the effectiveness of the virtual sensor approach. Additionally, the newly proposed Gramian-based observability criterion outperforms state-of-the-art metrics for sensor placement in terms of estimation accuracy for particular states of interest, as demonstrated in a sensor placement task. Overall, this paper provides a promising approach for virtual sensing for vibroacoustic applications, enabling accurate and efficient system monitoring and control. [Display omitted] • Virtual sensor for vibroacoustics using modal state-space models & sparse measurements. • Fusion of acoustical and structural data via a Kalman filter. • Novel Gramian-based observability metric improves the estimation accuracy. • Investigating the relation between the observability in modal and physical coordinates. [ABSTRACT FROM AUTHOR]

[Display omitted] • A damage hysteresis dynamic model of tangled metal was proposed for analyzing the damage incurred by TMW under cycles. • The Damage hysteresis dynamic model was constructed from which the coupled relationship between micro-damage and macroscopic performance degradation in the TMW. This model provides insights into the dynamic damage and life prediction modeling of the TMW. • Based on the higher-order dry friction model that considers the internal structural characteristics and process parameters of TMW, this study further proposed a TMW damage hysteresis dynamic model that characterizes the evolution of internal damage in the TMW. Tangled metal wire (TMW) is a kind of porous hyperplastic high damping material, which can realize the vibration isolation of key equipment. However, the unclear mapping relationship between internal microscopic damage and overall macroscopic performance made it difficult to construct a damage hysteresis dynamic model. This limits the application and development of TMW vibration isolation. In this paper, a strategy to analyze the dynamic damage evolution of TMW was proposed. Firstly, a cross-scale relationship between internal damage in TMW and macroscopic performance degradation was established. Furthermore, a damage hysteresis dynamic model was constructed by considering the high-order dry friction (HDF). The complex spatial spiral structure and preparation process parameters were described by using the HDF. Then, the damage factors of each component force were built and analyzed: The damage factor of HDF could be used to characterize the damage evolution of the metal wire inside TMW and the cross-scale correlation between microscopic damage and macro performance degradation, whereas the degree of hysteresis characteristics of TMW could be characterized by the damage factor of hysteretic coulomb damper. Finally, the damage hysteresis dynamic model was evaluated through residual analysis, and it has high prediction accuracy in more than 80 % of the cycles R 2 > 0.7. This paper provided a new idea for the modeling of TMW life prediction. [ABSTRACT FROM AUTHOR]

Mechanical joints play a critical role in many engineering structures, but identifying their characteristics can be challenging, particularly when the joint interface (coupling DoFs) cannot be directly measured. In these cases, an existing iterative procedure based on the use of the System Equivalent Model Mixing (SEMM) expansion technique and substructure decoupling can be used to identify the joint properties by taking measurement on other accessible DoFs (internal DoFs). Despite the potential of this procedure, it is prone to large error propagation. In addition, in the SEMM expansion a weighted pseudo-inverse operation is needed to ensure the convergence of the iterative procedure when both coupling and internal DoFs (extended interface) are involved in the decoupling. This paper focuses on the detection of the error sources in the process and on the definition of some strategies to limit error propagation. The use of internal DoFs only (pseudo-interface) in the decoupling is proposed. This avoids the use of coupling DoFs affected by the expansion error. Furthermore, two strategies are proposed to improve the conditioning of the procedure when using the extended interface. Both strategies are based on the Truncated Singular Values Decomposition (TSVD). It is shown that the weights clearly indicate the number of singular values to be retained in the matrices to be inverted. The proposed improvements are validated on a laboratory benchmark. Measurements on the benchmark are performed to validate the strategies with experimental data. In addition, the Monte Carlo method is applied using noise-polluted numerical data to evaluate the potential of the proposed strategies to mitigate the error propagation in the SEMM-based joint identification procedure. • This paper highlights and discusses the error propagation in SEMM-based joint identification procedure. • The use of the pseudo-interface in decoupling step and a fitting procedure are proposed to limit the error propagation. • Two strategies based on the TSVD are introduced to improve the quality of the joint identification when using the extended interface in decoupling. • Numerical and experimental examples are used to evaluate the proposed improvements. [ABSTRACT FROM AUTHOR]

Since rolling bearings frequently operate in variable speed conditions, the elastic deformation of the cage occurs intensely due to the enhanced excitation, which may lead to dynamic instability or even failure of the bearing. To this end, a flexible cage model based on the semi-flexible body element is developed in this paper. The force excitation from the ball and the guiding ring is considered to be applied to the cage. By coupling with the previous bearing model, a bearing dynamics model with flexible cage is presented. The results predicted by this model are closer to the experimental values than those of the rigid model. The differences between the two models in predicting the bearing dynamic performance under typical variable speed conditions are compared. The effect of preload on its dynamic behavior was further investigated with the flexible model. The results show that a slightly smaller radius of the cage orbit is predicted by the flexible model than that of the rigid model. Compared with the rigid model, more stability of cage motion is predicted by the flexible model. A similar phenomenon can be observed in the force excitation on the cage. There is not significant difference between these two models in predicting the velocity of the bearing assembly. The work in this paper provides a new idea for modeling flexible cages. [ABSTRACT FROM AUTHOR]

• A novel metamaterial isolator with a tunable low frequency stop-band was designed and prototyped. • The magnetic springs improve the low frequency vibration isolation performance. • The experimental results conform to the theoretical analysis of the cut-off frequency. This paper proposed and prototyped a metamaterial isolator with periodic structure based on magnetorheological elastomer (MRE) and magnet spring. With acoustic metamaterial structure, the proposed metamaterial isolator can generate a stop-band, which means the vibration over specific frequency can't be transferred and will be isolated effectively. Then, with the control of MRE stiffness, the cut-off frequency of the stop-band is tunable, which makes the isolator potential to be adapted to different working situations. The negative stiffness generated by the magnet spring can help to lower the start frequency of the stop-band and enhance the bearing capacity of the metamaterial isolator in vertical direction in the meantime. To verify the feasibility of the proposed device, the magnetic field simulation was first conducted and discussed, and the negative stiffness property of the magnet spring was numerically measured and demonstrated. Then, the mass-spring model of the metamaterial isolator was established in order to theoretically analyze the influence of MRE and the magnet spring on the vibration attenuation efficiency. A vibration platform was finally set up and the evaluation experiments were conducted to validate the theoretical analysis. In this paper, the structure design, theoretical modelling and experimental results of the metamaterial isolator are comprehensively discussed, which demonstrate that the prototype structure can work well as expected. The design of the metamaterial isolator with negative stiffness magnet spring can also enlighten more studies on lowering the bandgap frequency of the acoustic metamaterial without sacrificing large structure stiffness. [ABSTRACT FROM AUTHOR]

This paper presents a new framework for stochastic updating of a finite element model for a composite plate, considering the influence of temperature on Lamb wave propagation. The framework involves deterministic updating to optimize mechanical properties and stochastic updating to derive probability density functions for key parameters. It utilizes sensitivity analysis and Bayesian inference with Markov-Chain Monte Carlo simulations and the Metropolis–Hastings sampling algorithm. This paper proposes a machine learning surrogate model based on artificial neural networks to improve computational efficiency. This surrogate modeling approach allows parallelized Monte Carlo simulations, reducing updating time significantly without compromising the accuracy of the resulting probability density functions for model parameters. These advancements show a promising way to enhance composite plate modeling and Lamb wave propagation studies, providing a more efficient and accurate approach to verify and validate finite element models with potential applications in engineering simulations. [ABSTRACT FROM AUTHOR]

• A reinforced concrete moment-resisting frame was tested on a shaking table. • Table excitations and frame response accelerations with increasing intensities of the table motion were acquired. • Crack images and three damage-sensitive features are compared in various stages of the damage. • The results explore the dependence of the damage-sensitive features on horizontal peak table velocities. Methods of early warning in damage detection of civil engineering structures represent a particular challenge in Structural Health Monitoring (SHM). To investigate these problems, vibration records of a full-size, reinforced concrete frame subjected to seismic damages on a shaking table, were investigated. The seismic recordings of excitations and response were accompanied by images of cracks associated with each state of the damage. This paper presents the results of the damage identification from non-linear response during the damaging phases of the shaking table tests. Time-dependent base shear forces were observed, together with wavelet spectral analyses of the responses. Three damage-sensitive features accounting for decreasing stiffness, energy dissipation, and the wavelet changes during inelastic seismic responses were analyzed as functions of horizontal peak table velocities. The paper aims to study seismic response records of moment-resisting reinforced concrete structure frames in search of prospective simple online damage indicators which could be applied to start alarms, right after excessive damage loads, e.g., in instantaneous earthquake rapid damage assessments of buildings. [ABSTRACT FROM AUTHOR]

As we have known that it is impossible to attenuate the vibration displacement of the floating-slab depending on a strong stiffness and to improve the low-frequency vibration isolation performance of the floating-slab track (FST) depending on a soft stiffness simultaneously in engineering. In this paper, we propose a novel method to solve the paradox by introducing a "Hardening-First-Then-Softening" (HFTS) design based upon the energy distribution of wheel-rail loads to formulate a model of nonlinear floating-slab isolator. The average procedure is employed to analyze the vibration isolation ability of the isolator with parameters taken from a metro line load excitation. A simulation model of train-FST-tunnel with HFTS isolator is constructed to study the dynamic behaviors of the system. The numerical simulations show the significant suppression of displacement response of floating-slab and Z weighted acceleration vibration level of the tunnel. The results presented herein this paper provide a scheme to solve the strict requirements of displacement suppression and the vibration isolation which is of beneficial to steady and silent urban rail transit. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for damage detection in carbon fibre reinforced polymer (CFRP) structures based on local strain feature matching. The proposed approach utilizes distributed in-plane strain measurements for plate structures under load (in-service application) to define a local point autocorrelation coefficient based on Delaunay triangulation and Hausdorff distance. This coefficient effectively identifies the discontinuities in distributed in-plate strain, enabling accurate capture of damage information through the damage index which is defined as the integral of this coefficient. Notably, the proposed approach requires no prior knowledge of the non-damaged strain distribution and yields excellent detection results for both Barely Visible Impact Damage (BVID) and Visible Impact Damage (VID). Leveraging the advantages of distributed strain sensing, coupled with image processing technology for the purpose of in-service damage detection of CFRP structures, the proposed novel method is a promising technology. The efficacy of this technology has been demonstrated in this paper through theoretical analysis and experimental investigations, offering valuable insights and reference for future research in this domain. • Feature matching of local strain distributed is adopted in damage detection. • An autocorrelation coefficient is defined as a feature to effectively detect damages. • The proposed approach detects damage without prior non-damaged strain information. [ABSTRACT FROM AUTHOR]

• A novel unscented Kalman filter without direct feedthrough is proposed for the identification of joint input-system without direct feedthrough. • According to the uncertainty of the identified unknown inputs, two algorithms of the proposed method are presented. • The proposed method is verified by linear/nonlinear hysteretic shear structures under external or seismic excitations. Given the difficulty of accurately measuring external inputs acting on structures, the research on joint input-system identification has gained significant attention in recent years. Among them, several unscented Kalman filters under unknown input (UKF-UI) based methods have been proposed for joint input and system identification. However, the existing UKF-UI methods require unknown inputs to be presented in the observation equations, which is not always available in practical engineering. To solve this problem, an unscented Kalman filter under unknown input without direct feedthrough (UKF-UI-WDF) is proposed in this paper for real-time joint input and system identification of structural systems without direct feedthrough using limited response measurements. The proposed UKF-UI-WDF is derived for recursive identification of the structural states, parameters, and unknown inputs by integrating unknown input estimation into the conventional UKF framework. In each identification step, the unknown inputs are first identified by minimizing posterior residuals of measurements, and then the structural states and parameters are identified by a traditional unscented Kalman filtering using the identified unknown inputs. According to whether the uncertainty of the identified unknown inputs can be quantitatively described or not, two implementation algorithms are provided in this paper. In algorithm 1, only the mean values of unknown inputs can be identified. In algorithm 2, the unknown inputs are identified as a set of sigma points, so not only the mean values but also the variance of the unknown inputs can be estimated. The effectiveness of the proposed method is examined by the joint input and system identification of various case studies of linear and nonlinear hysteretic shear structures subjected to unknown external or base excitations, respectively. The results show that the proposed UKF-UI-WDF method can effectively perform real-time joint input-system identification of structural systems without direct feedthrough using limited response measurements. [ABSTRACT FROM AUTHOR]

• A novel HI is proposed through correlation warping radius tracking. • The proposed HI is applicable for variable operating conditions. • The proposed HI is effective to characterize the degradation process. • The proposed method is validated sensitive to incipient defect. Health indicators (HI) are crucial in early fault alarm and degradation monitoring of mechanical failures. In recent years, many HIs are developed and reported, however, a constructed HI which is more sensitive to incipient defect, robust to varying operating conditions and is interpretable to characterize the degradation process is still remain to be studied. To address this issue, a more powerful and effective HI applicable for nonstationary operating condition is studied in this paper. Inspired by chaos fractal theory (CFT) and slowly varying damage dynamics theory (SVDDT), this paper calculates the range of correlation warping radius (CWR) of each segment of signal through phase space reconstruction, and then finds that the boundary of correlation warping radius range (CWR-B) in normal conditions follows a fixed linear relationship. Through the application of this linear relationship, a new health monitoring indicator for vibration signals of rotating machinery has been proposed, through correlation warping radius tracking measurement (CWRTM). The proposed indicator not only has outstanding monitoring advantages for rotating machinery in a cyclo -stationary condition, but also applicable for machinery condition monitoring under variable operating conditions, achieving the unity of methods for condition monitoring in stationary and variable operating conditions. Compared with some typical health indicators, the experimental results on bearing run-to-failure dataset and wind turbine generator bearing field dataset show that the proposed indicator in this article is not only sensitive to early-stage fault, but also validated robust to varying operating conditions. [ABSTRACT FROM AUTHOR]

Latching control is a phase optimization control mechanism for improving the energy conversion efficiency of a wave energy converter (WEC). This paper, for the first time, extends the latching mechanism to vibration control to improve energy dissipation efficiency. An innovative semi-active latched mass damper (LMD) is proposed in this paper by integrating conventional tuned mass dampers (TMD) and the latching mechanism. First, the theoretical models of WEC and TMD are compared. Second, the mathematical model of an LMD installed on a single-degree-of-freedom structure is established, and three latching control strategies with different latching time instants, latching durations, unlatching time instants, and required information feedback are tested. Subsequently, the performance of the LMD is evaluated through the comparison with the detuned and optimally tuned TMDs. The characteristics of the latching control force are analyzed in terms of phase, component, energy dissipation, and damping enhancement effect. Lastly, the feasibility and effectiveness of the proposed LMD with three different latching strategies are proven. The latching control can reduce the stiffness component and increase the damping component of the control force, thereby optimizing the phase lag between the control force and the structural response. The proposed latching mechanism and strategies for vibration control can provide an innovative solution to improve the effectiveness of high-frequency detuned dampers, facilitate the design of long-period dampers, and develop adaptive semi-active dampers in the future. [ABSTRACT FROM AUTHOR]

The parasitic motion of microgripper jaws affect clamping accuracy. The parallelogram mechanism (PM) is often used as the last-stage of the multi-stage amplification mechanisms (AMs) because of its parallel clamping characteristics. However, the PM traditionally used in multi-stage AMs is asymmetrically stressed, which will affect its parallel clamping accuracy. Based on this, a PM with symmetrical forces is designed. The paper addresses the issue of parasitic motion in microgripper jaws and its impact on clamping accuracy, which is a relevant concern in micro-scale manipulation systems. The design of a symmetrically stressed PM for use in a two-stage asymmetric microgripper is a novel contribution to the field. Based on the minimum potential energy principle and the Castigliano's second theorem, the mechanical modeling of the mechanism's parasitic displacement, magnification and parallel clamping accuracy is carried out. Comparing the mechanical model, finite element analysis (FEA) and experimental measurement results, the values given by the three different methods are close. The method given in this paper to improve the clamping accuracy of the mechanism is also applicable to similar compliant mechanisms. [ABSTRACT FROM AUTHOR]

The electromechanical impedance (EMI) method has shown its damage detection capability for lightweight structures due to its damage sensitivity. In this paper, a new technique based on the uniform sensor network for multiple damage localization was proposed. The EMI-based damage indices were obtained from several piezoelectric transducers and fused for multiple-damage localization for the first time in literature. In the proposed approach, the sensor sub-network selection employs the vertices of a triangle based on the calculated sensing region radius. The method employs sub-networks to allow multiple damage sites localization without prior information about the number of damage sites to be found. These sub-networks approaches were applied to detect and localize the two and three damaged sites simultaneously. The glass fiber reinforced polymer (GFRP) and 3D-printed acrylonitrile butadiene styrene (ABS) plates were used for the investigation, with surface-bonded piezoelectric transducers (PZTs). Magnets were used to simulate multiple-damage positions, and drilled holes were used as more realistic multiple-damage. In the investigated cases the EMI data displays damage-sensitive peaks in a frequency range of 1 kHz to 200 kHz. The results show successfully the detection and localization of multiple-damage sites in additively manufactured (AM) polymer and fiber-reinforced composite plates. It was observed that the damage localization error increases significantly with the new damage introduced in the structures. [ABSTRACT FROM AUTHOR]

Vibration issues in cylindrical shells are prevalent in engineering applications and can significantly impact the operational performance of systems. To address these challenges, this paper designs a novel local resonance tower structure, and establishes an analytical model to investigate vibration characteristics of the metamaterial cylindrical shell. Based on the model, a gradient design strategy is proposed to improve vibration suppression properties over a wide frequency range. Firstly, the governing equations of the cylindrical shell are derived and the dispersion analysis is conducted to predict the bandgap boundaries using the extended plane wave expansion (EPWE) method. Then, for a finite-period metamaterial cylindrical shell with specific boundary conditions, the frequency response of the structure is obtained by finite element calculations to show the bandgap behavior. The results of these two methods are basically the same. In addition, the effects of frequency spacing, damping and array period on the vibration characteristics are considered comprehensively. The results show that a wider and more stable attenuation range can be obtained by reasonably adjusting these three parameters. Finally, the comparison between experimental and numerical calculations verify that the tower structure is effective, and that the proposed design strategy can broaden the vibration attenuation region. [ABSTRACT FROM AUTHOR]

Passive chatter suppression by addition of tuned mass dampers has proven effective in enhancing machining stability of thin-walled workpieces. However, the damper installed on a fixed position may fail to offer effective and robust chatter suppression, as the cutting position varies during machining. This challenge is compounded by the steep dynamics variation of the workpiece along the radial direction caused by material removal in cutting zones. The present paper presents a comprehensive study on improving the chatter stability of milling thin-walled casing workpieces by employing a sucker-mounted damper, which allows a quick and easy repositioning for following the instantaneous cutting zone. An inverse multi-point receptance coupling method is developed to predict the frequency response function of the workpiece equipped with the sucker-mounted damper at the instantaneous cutting position, while accounting for the material removal effect. The damper's parameters are optimized using a new strategy focused on stability lobe diagrams to maximize the chatter-free cutting depth within a specified spindle speed range. This strategy aims to maximize material removal rate while adhering to the constrained speed range imposed by practical processing systems. The structure of damper, which embodies the optimum parameters, is implemented from configuration to dimensions using the screw theory. A series of modal test and milling experiment with and without the damper are conducted to verify the validity and reliability of the sucker-mounted damper. The results indicate that the damper is able to improve the material removal rate by up to 220.0%, and satisfy the requirement of robust performance, flexible repositioning and secure attachment for milling large thin-walled workpieces. [ABSTRACT FROM AUTHOR]

• A search-vector imaging condition is proposed based on waveform inversion. • The imaging condition makes no assumption about the wavefield and is more rigorous. • The proposed method highlights the defects in the measurement area with high accuracy. • The proposed method greatly improves the imaging accuracy, resolution and SNR. • The proposed method is strongly robust and not sensitive to various factors. Complex-surface components play more and more significant roles in engineering practice because of the development of manufacturing technology and requirements from various industries. Void and crack defects inside the components greatly deteriorate the mechanical properties, and ultrasonic full-matrix imaging is a promising method to detect the defects. However, the research on high-quality full-matrix imaging for complex-surface components is limited. In this paper, a full-matrix imaging method based on the search-vector imaging condition is proposed to detect the defects in complex-surface components with high accuracy. Herein, the search-vector imaging condition is established based on waveform inversion, which employs the first-order derivative vector and approximated Hessian matrix to obtain the high-accuracy images of defects. The proposed imaging condition does not make assumptions about the wavefield and is more rigorous than the conventional excitation-time imaging condition. Furthermore, the proposed method can highlight defects throughout the measurement area, so it is very convenient for locating defects in large measurement structures. In the simulation and experiments, defects in complex sound speed models are the measurement targets, and the proposed method has significantly improved the imaging accuracy, resolution and signal-to-noise ratio (SNR) in all cases compared with the conventional methods, where all the void and crack defects are highlighted clearly in the whole image. In addition, the proposed method is not sensitive to the influencing factors and has strong robustness. Therefore, the proposed method has great potential in defect detection for complex-surface components in various fields. [ABSTRACT FROM AUTHOR]

• An efficient hybrid semi-analytical and finite element method for fluid-transporting cracked pipe is proposed. • Bending-bending and bending-axial coupling responses are reported to detect crack faults. • Effects of crack and fluid parameters on the dynamic characteristics of the cracked pipe are analyzed. The fatigue cracks may develop in the pipe systems due to long-term cyclic loading, which may cause catastrophic accidents. The finite element method (FEM) is usually adopted to simulate the stress singularity and breathing effect, resulting in expensive computational costs. Therefore, in this paper, an efficient hybrid semi-analytical and finite element (SA-FE) method is proposed to study the nonlinear dynamic characteristics of the fluid-transporting cracked pipe (FTCP) considering the breathing effect. Furthermore, the crack sensitivity parameters are obtained through bending-bending and bending-axial vibration analysis. For the SA-FE method, the semi-analytical method (SAM) and FEM are separately used to construct the dynamic models of the non-cracked and cracked regions, and the connection at the interface is performed by constraint equations. Subsequently, the validity of the modeling method is substantiated through both FEM and experimental tests. Finally, the effects of crack and fluid parameters on the dynamic characteristics of the FTCP are analyzed. The results indicate that changes in crack positions may trigger different-order mode switching, and continuous switching may occur between higher-order modes. In addition, the bending-bending and bending-axial coupling responses can effectively detect crack failures of the FTCP with varying levels of damage. The proposed model can offer valuable insights for fault diagnosis and health monitoring of FTCP. [ABSTRACT FROM AUTHOR]

• IE models and AGs are used to describe and compare a set of six real bridge decks bridges. • Modal responses from each of the six bridges are identified and subsequently compared using the Modal Assurance Criterion. • increasing number of elements making up the beam and slab bridge decks results in a consistent decreasing similarity metric • Similarity metrics from Fig. 7 appear largely reflective of the meaningful similarities between the bridges Bridges are valuable infrastructure assets that are challenging and expensive to maintain. State-of-the-art data-based bridge SHM solutions look to use bridge response data for condition assessment and damage detection. Data-based SHM methods can be limited in their application as they require large datasets to train models effectively, and most bridges lack the available data for the approaches to work. Further, it would be expensive and unrealistic to collect the required datasets to employ data-based methods to entire bridge networks. Recently, a population-based structural health monitoring (PBSHM) approach was proposed that seeks to leverage the data available for SHM problems by pooling together similar structures with their datasets. The PBSHM approach could be valuable in bridge SHM, enhancing the datasets available for 'populations of bridges. The PBSHM approach for assessing the similarity of bridges has been considered before and was shown to be useful for identifying similar and different bridge types. However, no data were considered in the previous work, and similarity metrics were only qualified using engineering judgement. For the PBSHM approach to be useful in bridge SHM, there is still a need to check that 'similar' bridges have similar responses for transfer learning to be feasible. This paper expands upon previous work and provides originality by investigating if bridges identified as similar also exhibit similar responses. The PBSHM derived similarity metrics convey the topological similarity between structures, and mode shapes are identified as being a topologically sensitive bridge response. Therefore, a modal test campaign is carried out for a set of six real bridges, and Operational Modal Analysis is used to identify modal responses from each of the bridge decks. The Modal Assurance Criterion is used to evaluate the similarity between the mode shapes from pairs of bridges and is subsequently compared to the similarity metrics evaluated between those bridges. The similarity metrics were found to be reflective of the similarities identified between the respective bridges' mode shapes for bridges of the same and different types. The significance of this finding is that it is an important step towards validating the PBSHM comparison approach for identify similar structures where transfer learning might be attempted. [ABSTRACT FROM AUTHOR]

Virtual sensing methods (VS) enable an active noise control (ANC) system to achieve noise reduction at a target control point, but impractical to place an error microphone for an extended period of time. This is realized by employing remote monitoring microphones. This paper introduces the relative path based VS (RP-VS) method for feedback ANC systems and compares it to existing VS methods, including the auxiliary filter based VS (AF-VS) method and the remote microphone based VS (RM-VS) method. Theoretical analysis is conducted to describe the impact of acoustic path changes on their noise reduction capabilities. When acoustic paths remain constant, all three VS methods perform similarly. However, they behave differently when acoustic paths change: the RP-VS method aligns with the AF-VS method, while the RM-VS method matches the performance of the fixed-coefficient (FC) filter. Moreover, changes in the primary noise characteristics can be treated equivalently as changes in the secondary paths at a single frequency, but these equivalent changes are inherently uncertain. Simulations and experiments conducted on an active headrest hence demonstrate the robustness of the RP-VS method against changing paths and primary noise characteristics. • A new virtual sensing method for feedback active noise control is proposed. • A unified analysis for three virtual sensing methods, including the proposed one, is presented. • The results contribute new insights into virtual sensing methods for feedback active noise control. • Lab-built active feedback headrest validates the proposed virtual sensing method. [ABSTRACT FROM AUTHOR]

In the realm of mechanical machining, adaptive machining techniques offer an efficient method. However, existing adaptive machining technologies cannot automatically identify the type of workpiece to be machined, nor can they set different control targets based on tool load-bearing capacity. This often requires manual and cumbersome operations, making the application of adaptive machining technology difficult to popularize. To address these challenges, this paper proposes an intelligent and efficient adaptive machining method. Specifically, a workpiece automatic classification algorithm based on machine tool spindle power is introduced. This algorithm classifies the machined workpieces according to the collected spindle power data, enhancing the convenience of adaptive machining technology. Also based on spindle power data, a time-series segmentation method is proposed, dividing spindle power into different subsequences according to the tools used in machining, which enhances the accuracy of adaptive machining technology. Furthermore, an adaptive feed rate control algorithm is designed based on fuzzy control theory, realizing intelligent and efficient adaptive machining of workpieces to improve machining efficiency. Finally, experiments are conducted to validate the effectiveness of the intelligent and efficient adaptive machining method. The proposed method effectively addresses the drawbacks of existing adaptive machining method, which require complex parameter settings. It simplifies the operational process, enhances the intelligence, and improves the practical applicability of adaptive machining techniques. This represents an innovative contribution to the field of adaptive machining techniques. [ABSTRACT FROM AUTHOR]

In this paper, a novel blind source separation (BSS) method drawing on the framework of Multivariate nonlinear chirp mode decomposition (MNCMD) termed as Nonlinear Chirp Component Analysis (NCCA) is proposed. In contrast to other BSS methods, NCCA is designed to separate the non-stationary sources in an elegant variational optimization framework. Firstly, the source signals are modeled as wide-band nonlinear chirp modes (NCMs) which could be transformed into narrow-band signals with demodulation techniques. Afterwards, the objective function is represented as the summation of source signals bandwidths fulfilling the constraint that the multivariate observed signal is the linear combination of the source signals. Finally, the alternate direction method of multipliers (ADMM) algorithm is employed to solve this optimization problem without more predefined parameters than MNCMD. After that, the source signals including their instantaneous frequency (IF), instantaneous amplitude (IA), the mixing matrix are updated to the optimal values. The merits of NCCA inherited from MNCMD like mode-aligned, filter bank structure, Quasi-orthogonality of modes and its unique attribute of identifying the BSS model and undetermined blind source separation (UBSS) model are testified through a series of synthetic signal. Its anti-noise property and convergence property get verified too. The performance of NCCA in the analysis of vibration analysis through simulation and experimental vibration system is highlighted in the end. [ABSTRACT FROM AUTHOR]

Deep Neural Network (DNN) models offer an attractive alternative to existing acoustic source imaging techniques, such as acoustic beamforming, due to their ever-growing potential with increasing computational power. Source resolution of acoustic beamforming methods is limited at lower frequencies and their source maps may possess sidelobes at higher frequencies. However, acoustic beamforming methods are typically robust over a wide range of simulation and experimental conditions, such as (i) the number of sources present, (ii) source frequency and (ii) extraneous noise sources. The performance of DNN models, when these conditions are varied from their specific design criteria, is yet to be investigated and much work is needed in this area before DNN models can be utilized in experiments, such as wind tunnel tests. Furthermore, few studies have been conducted on experimental validation of DNN models, predominately due to the difficulty of large sets of experimentally obtained data needed for DNN model training and the sensitivity of DNN model performance when any of the aforementioned experimental conditions are varied. In this paper, a series of studies on the robustness of DNN models based on numerical data and experimental data are presented. Numerical DNN (NDNN) models are trained using in-phase and random-phase pressure data generated from six sources over design frequencies from 500 Hz to 20,000 Hz. The robustness of the NDNN models is tested via (1) inclusion of extraneous Gaussian white noise, (2) inclusion of extraneous tonal noise near the design frequency, (3) using source frequencies that slightly differ from the design frequencies and (4) using a number of sources that differs from the design source number. DNN model performance metrics are introduced that present a promising framework for future DNN model studies and bridging the gap between NDNN and experimentally trained DNN models. A preliminary experimental validation was conducted using a single speaker that was systematically placed over a speaker grid to generate training data via acoustic superposition, from which an experimentally trained DNN (EDNN) model was produced. The EDNN model yields exceptional noise source localization capability of the DNN model, revealing a promising start for a more sophisticated EDNN model. • Robustness of a numerical DNN model is analyzed for conditions away from its design criteria. • Various conditions are tested, e.g., source number, source frequency, extraneous noise sources. • The numerical DNN model is trained using both in-phase and random phase sources. • Experimental validation is conducted through an experimentally trained DNN model. • The experimental DNN model yields exceptional noise source localization capability. [ABSTRACT FROM AUTHOR]

• Extend LHPM to non-stationary non-Gaussian cross-correlated fields. • Establish transformation model from non-stationary non-Gaussian CMF to the underlying Gaussian CMF. • Represent the underlying Gaussian cross-correlated fields by multivariate K-L expansion. • Compute eigenvalues and eigenfunctions using Nyström method. • Develop a unified simulation framework for non-stationary non-Gaussian cross-correlated fields. In practical engineering, cross-correlated random fields are often used to model structural materials or random loads containing multiple correlations. Effective and accurate simulation of these cross-correlation fields is an important prerequisite for subsequent reliability analysis and uncertainty quantification of complex systems. Therefore, this paper proposes a new simulation method for non-stationary non-Gaussian cross-correlated random fields to satisfy realistic engineering requirements. In this new method, L-moments-based Hermite polynomials model (LHPM) is extended to non-stationary non-Gaussian cross-correlated fields. Then, a transformation model from non-stationary non-Gaussian correlation matrix function (CMF) to the underlying Gaussian CMF is explicitly given. Multivariate K-L expansion is further used to approximate the underlying Gaussian cross-correlated fields, where the eigenvalue and eigenfunctions are estimated via the Nyström method with uniform weights. The approximation permits the application of few random variables to characterize the entire Gaussian cross-correlated fields. Ultimately, the simulated underlying Gaussian cross-correlated fields are mapped back to the target non-stationary non-Gaussian cross-correlated fields based on LHPM. Three typical examples, including exponential kernel, Wiener process kernel and spatially varying non-Gaussian and nonstationary seismic ground motions, are used to validate the effectiveness of the proposed method. The source code is readily available at: https://github.com/zhaozhao23/Simulation-of-non-stationary-and-non-Gaussian-cross-correlated-fields. [ABSTRACT FROM AUTHOR]