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This paper presents a simulation method that is used to study subcritical vibrations of a tube roll in a paper machine. This study employs asymmetric 3D beam elements based on the Timoshenko beam theory. An asymmetric beam model accounts for varying stiffness and mass distributions. Additionally, a detailed rolling element bearing model defines the excitations arising from the set of spherical roller bearings at both ends of the rotor. The results obtained from the simulation model are compared against the results from the measurements. The results indicate that the waviness of the bearing rolling surfaces contributes significantly to the subcritical vibrations while the asymmetric properties of the tube roll have only a fractional effect on the studied vibrations. [ABSTRACT FROM AUTHOR]

• 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]

With the development of urban rail transit, environmental vibration caused by trains has garnered increasing attention. In the research on environmental vibration induced by trains, commonly employed methods include physics-based models grounded in mathematical principles, transfer function approaches and deep learning methods based on experimental data. Among these methods, physics-based models based on mathematical principles can elucidate the excitation mechanism and provide physically meaningful peaks, such as P2 wheel-rail resonance force, bogie passage frequency, and so on. However, their drawback lies in the difficulty of real-time parameter matching with actual parameters. On the other hand, data-driven methods, while capable of predicting environmental vibrations under train operation through extensive data, face challenges in discerning the randomness and authenticity of the data. This can result in numerically meaningful peaks in deep learning lacking physical significance and even lead to a model misrepresentation. Moreover, there is a lack of detailed classification based on the excitation mechanisms of environmental vibration, which results in a relatively low accuracy in predicting environmental vibrations caused by train-induced activities. Therefore, this paper, based on the excitation mechanisms of environmental vibrations and on-site measured data, employs a physical numerical model to clean and summarize the distribution patterns of the test data. Subsequently, using Self-Organizing Maps, the vibration signals are classified according to the train states. For each category, a neural network system based on Bayesian regularization is applied to establish the input–output function relationship of environmental vibrations. Finally, this paper predicts the metro-induced environmental vibration. Through experimental predictions of the observation samples, it is evident that the prediction method proposed in this paper reduces the predictive variance by 23 times compared to direct neural network fitting, resulting in a 3 times improvement in prediction accuracy. [ABSTRACT FROM AUTHOR]

• Breaking through the traditional thinking of treating the input energy of EMAT as a single whole, this paper innovatively divides the input energy into multiple dimensions and regulates it. • The SNR and spatial resolution of the damage detection system with SH0 wave are improved using spatiotemporal pulse compression technology (STPCT) and staggered dual-exciter. • The double sidelobe suppression algorithm effectively mitigates sidelobe issues caused by STPCT. The fundamental shear horizontal (SH0) wave, which can be generated by electromagnetic acoustic transducers (EMATs), is widely used in the structural health monitoring fields owing to its non-dispersive nature and the ability to detect over long distances. However, current SH0 wave methods hardly achieve the excellent signal-to-noise ratio (SNR) of the echo signals and satisfactory spatial resolution to defects due to the co-restriction between them. To address these issues, a spatiotemporal energy regulation strategy (STERS) is proposed in this paper to optimize energy distribution in the temporal and spatial dimensions. First, spatiotemporal pulse compression technology (STPCT) is proposed to improve both SNR and spatial range resolution for defect detection. Second, a staggered dual-exciter employing two sets of excitation signals is designed to improve the spatial azimuth resolution. Besides, a double sidelobe suppression algorithm (DSSA) is proposed to suppress sidelobe issues arising from the application of STPCT, thereby further improving the performance of STPCT. Finally, multiple simulations and experiments are executed to demonstrate the advantages of the proposed damage detection system. Results show that our methods can achieve comprehensive improvement in SNR, spatial range resolution, spatial azimuth resolution, and sidelobe suppression ability. [ABSTRACT FROM AUTHOR]

• The influence of machining vibration and process parameter on the surface roughness is considered in the prediction model. • Highly correlated features to surface roughness are extracted using combined GRA and MIC. • Extreme learning machine algorithm with high accuracy and efficiency is applied to predict the machined workpiece surface roughness. • The predictive surface roughness at the entire processing area is achieved by the proposed method. Surface roughness of mechanical parts plays an essential role in the practical application. Due to the complexity of the machining process, establishing a comprehensive theoretical model that can accurately simulate the real machined surface is challenging. Therefore, data-driven prediction methods are both practically useful and scientifically sound. This paper proposes an approach of using extreme learning machine (ELM) to predict surface roughness based on the monitored vibration signals, which can achieve the prediction of the surface roughness at any position of the machined surface, rather than using a single value to evaluate the processing quality under specific situations. To ensure the accuracy of the vibration intensity representation, the amplitude of monitored signals is compensated, accounting for varying positions and timings. The linear relationship between the spindle speed and acceleration is investigated to mitigate the impact of machining parameters on the monitored acceleration. Combined with grey relational analysis and mutual information coefficient, 18 features substantially linked to the surface roughness are selected from all extracted features and then used as the inputs for an ELM-based predictive decision-making system. The results indicate that the average and minimum errors of the developed method are 5.09% and 1.03%, respectively, demonstrating its accuracy and feasibility. In contrast to other machine learning models of SVR and ANN, the proposed method in this paper exhibits optimal predictive precision and efficiency, suggesting its potential application in the fast response of quality control. [ABSTRACT FROM AUTHOR]

Dynamic soil-structure interaction (SSI) is an important field in civil engineering with applications in earthquake engineering, structural dynamics, and structural health monitoring (SHM). There is an ongoing need for the development of numerical methods that can accurately estimate SSI parameters to model these systems. In this paper, a Frequency Response Function (FRF)-based model updating method is developed that can estimate the embedded length of foundation piles, in addition to the mobilized soil mass and stiffness, when a lateral impact load is applied. Knowledge of the embedded length of piles is very important for modelling foundation behaviour, and may not be readily available from as-built construction information. For example, if developing reference damage models or digital twins of foundation structures, full knowledge of the pile geometry is required. The work in this paper develops a two-stage iterative model updating method, which utilizes FRF data obtained at the pile's head as a result of an applied lateral impact load. The method uses information from the 1st mode of vibration to estimate the mobilised soil mass and stiffness, and subsequently uses information from the 2nd mode of vibration to estimate the embedded length. To appraise the approach, impact tests are numerically simulated on a number of 'piles' (numerical spring-beam systems) with varying length/diameter (L/D) ratios to derive FRFs, whereby the models have known length and dynamic properties. These FRFs are then used as targets in the model updating approach, which iteratively varies the properties of a numerical model of a pile to obtain a match in the FRF data, and subsequently estimates the mobilised stiffness, mass, and embedded length. The results of the analyses illustrate that by minimising the difference in the first and second FRF peaks between the target and estimated FRFs, the method can accurately estimate the mass, stiffness and embedded length properties of the test 'piles'. The performance of the approach against numerical case applications is assessed in this paper, as the properties of these systems are known in advance, facilitating quantification of the errors and performance. The developed method requires further validation through full-scale testing to confirm its effectiveness in real-world scenarios. [ABSTRACT FROM AUTHOR]

Traditional piezoelectric energy harvesters cannot adapt to environmental energy with low frequency, wideband, and randomness because of the narrow working bandwidth. The introduction of magnetic coupling can improve the narrow-band characteristics of piezoelectric energy harvesting structures. To explore more efficient broadband methods, this paper designs a piezoelectric and electromagnetic hybrid energy harvester (PEEH) based on two-stage magnetic coupling to realize the expansion of piezoelectric energy harvesting bandwidth. In this paper, the magnetic pole alternating frequency is changed and the output of the structure is tested, and the operating frequency bands under different alternating frequencies are compared according to the test results. It can be seen from the experimental results that the output performance of the structure is obviously improved by increasing the AC frequency of the magnetic pole while reducing the optimal frequency point of the structure. When the number of magnets in the rotor magnet array is 6, increasing the pole alternating frequency can effectively improve the output performance of the electromagnetic unit when the rotor speed is low, and can also increase the maximum output voltage and energy harvest bandwidth of the piezoelectric unit by 189% and 150%, respectively. Therefore, by increasing the alternating frequency of magnetic poles, the output voltage of the structure can be improved, and the bandwidth of the structure can be broadened. [ABSTRACT FROM AUTHOR]

This paper considers the interaction between a rotating bladed-disc and static casing, specifically a synchronisation phenomenon called wave speed coincidence. Through tip rub, travelling waves in the bladed-disc and casing can interact and cause large amplitudes of vibration. Previous work has analysed this phenomenon using time integration methods, but these are often too computationally expensive for early design stages, especially if a large number of designs and conditions need to be considered. Frequency domain approaches such as the harmonic balance method have also been used but they required simplification in the periodicity of the problem which limits the usefulness of such methods. This paper demonstrates the use of a power method and consideration of periodicity over a contact cycle. This allows the problem to still be considered as a periodic behaviour, but without the loss of generality associated with conditional harmonic balance methods. The method is intended to act as a preliminary screening tool, to down-select cases with potential risks of instability due to wave speed coincidence. The smaller number of cases can then be checked using time integration methods. While the current power method is limited in capturing the bladed-disc dynamics, the results demonstrate its use and potential to be developed into a screening method for preliminary design stages. [ABSTRACT FROM AUTHOR]

Blade-casing rubbing is a typical fault of aero-engine, but due to the complex structure of the equipment, it is still challenging to diagnose. This paper studies the characteristics of rubbing faults under rotor stator coupling. Firstly, a numerical model, which includes the rotor, bearings, and casing with blades, is constructed. Secondly, simulate the rubbing fault by setting the non-uniform initial gap between the blades and the casing, and the vibration response of the rotor and casing is obtained. Thirdly, combined with the test rig, the vibration characteristics under rubbing fault are verified. Finally, additional theoretical research is conducted on the vibration characteristics of the rotor, casing and blades under different degrees of rubbing fault. Theoretical and experimental results indicate that rubbing faults have different effects on the vibration of the rotor and stator: the rotational frequency amplitude of the rotor decreases while the frequency amplitude of the stator increases. As the fault degree increases, these phenomena become more significant. When rubbing faults occur, the harmonics near the system's natural frequencies are more affected than the others. Moreover, rubbing faults can also cause the vibration of the rotor to be affected by the blade mode. The conclusions obtained in this paper can provide a reference for diagnosing rubbing faults in aero-engine. [ABSTRACT FROM AUTHOR]

• Diagnostic information in vibration signals is distorted by transfer functions. • Cepstrum-based OMA can be utilised to estimate forcing functions for SIMO systems. • In MIMO situations, responses to a CS2 source with unique cyclic frequency can be separated. • Previous method required phase unwrapping the CSD, but often not possible with excessive noise. • Proposed new method based on the real cyclic cepstrum avoids phase unwrapping the CSD. • Results of application to extraction of gearmesh forcing functions greatly improved. Operational modal analysis aims at identifying modal parameters of a system from response measurements only. Cepstrum-based operational modal analysis (OMA) has an advantage over conventional OMA methods, since it relaxes the assumption of white noise excitation and is able to retain the relative scaling between modes, which is important in many applications such as force identification. In previous research, the cepstrum-based OMA has been applied to reconstruct the forcing functions generated by a gear pair from response measurements, in order to obtain more direct diagnostic information. However, the cepstrum-based OMA can only cope with systems with a single excitation. To overcome this challenge, this paper proposes a novel technique, which can be applied to minimum-phase multiple input systems that have a cyclostationary excitation with a unique cyclic frequency. The technique first extracts the single-input system from the multiple-input case using the cyclic spectral density (CSD) of the measurement, equal to the spectral correlation density (SCD) evaluated at a particular cyclic frequency. The method then identifies the frequency response function (FRF) by fitting the real cyclic cepstrum of the measurement, this being the inverse Fourier transform of the log magnitude of the CSD. This method elaborates on a technique developed in previous research, which extracted the signal of interest from the complex cyclic cepstrum instead, this being the inverse Fourier transform of the logarithm of the CSD with unwrapped phase. In this paper, a limitation of the previously developed technique is identified and discussed for the first time, viz. problems with the phase unwrapping of the CSD. The technique in this paper overcomes the drawback in the previous research and exhibits better performance in noisy conditions. Both methods have been validated on synthetic signals as well as vibration measurements from a beam test rig. An attempt has also been made to apply them to vibration signals from a single-stage spur gear test rig. The method based on the real cyclic cepstrum is considered to have great potential to reconstruct the forcing functions from vibration signals measured from multi-stage gearboxes. [ABSTRACT FROM AUTHOR]

For the power propulsion system of ships, due to the disturbance of propeller rotation and its instability, sliding bearings will support the inclined shaft during operation, resulting in misaligned effects of the bearings. Traditional research on sliding bearings often uses laminar flow models, but there is turbulence in the actual operation of sliding bearings, so the turbulence model will conform to the actual structure. This article studies the impact of misalignment effects in different directions by comparing whether turbulence effects is considered. It should be noted that the maximum degree of misalignment is achieved under the condition of bidirectional misalignment. The alignment effect is considered as a reference. An analysis is conducted on sliding bearings under two flow conditions and four misalignment forms. The variation patterns and trends of static and dynamic characteristic parameters are obtained and analyzed. Summarized the impact of flow models and biased effects on various parameters. Finally, experimental tests are conducted on the bearings. Obtained the time domain, frequency spectrum, and axis trajectory of the bearing. The reliability and correctness of the current research have been verified by comparing experimental data with theoretical results. [Display omitted] • In this paper, we believe that our paper presents for the first time: • The modified lubrication model with/without turbulent effects & four misaligned statuses is constructed. • The effects of multi-coupling factors on static & dynamic characteristics are analyzed. • The changing rules of the lubrication performance with different misaligned status are revealed. • The rationality and correctness of the model and the algorithm have been verified. Turbulence has significant impacts on lubrication characteristics of the misaligned bearings. Modified lubrication models with turbulent & misaligned effects are constructed. The influences of velocity, load and lubricant viscosity on static & dynamic characteristic with two flow models and four misaligned cases are analyzed. The changing rules of the lubrication performance with different misaligned statuses are revealed. Comparative results reveal that the lubrication film thickness of misaligned bearings decreases, the friction force increases, and the lubrication performance decreases. The maximum pressure considering turbulence effects is smaller than that of the laminar-flow model, while minimum film thickness is thicker than that of laminar flow model. Results provide theoretical and practical basis for optimum design of such bearings. [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]

In industrial automation, the transportation of unit loads plays a crucial role. This paper addresses the under-explored area of plastic chain conveyors, a versatile and efficient means of transport within production processes. Despite their widespread use, these conveyors suffer from wear and tear due to continuous contact between the chain and guide rails, often leading to unscheduled shutdowns and increased costs. The literature on monitoring solutions for such systems is sparse, particularly for plastic chains. This paper aims to bridge this gap by surveying the most common wear mechanisms for these systems and presenting a physical model that describes the interaction between the chain links and guide rails, based on chain of masses, springs and dampers and the Archard wear model. This model allows to relate the variations due to wear with other physical quantities, such as passage frequencies of the links and heat generated due to friction. The comprehensive model provides analytical formulas to understand the vibrations induced by the chain on the platform and the guide-rail temperatures. To validate the model and evaluate how the measurements are affected by the degradation of the system, an extensive experimental campaign was conducted on a conveyor test rig, acquiring vibration and temperature data. The end goal is to identify and discuss methodologies suitable for estimating the wear mechanisms of conveyor systems, with a focus on the constraints of industrial environments. Hence, this study aims to lay the groundwork for defining monitoring solutions that enhance the maintenance efficiency of plastic chain conveyor systems. The results suggest that the power Spectral Densities (PSD) of the platform's accelerations are sufficient to estimate the average links pitch, which in turn could be used to assess the chain elongation. Besides, the local temperatures confirmed the model observations, making them suitable for estimating the slide-rail thinning. • Health monitoring of conveyor systems is crucial to ensure the transportation of goods. • Plastic Chain Conveyors are useful for complex paths but prone to wear by friction. • There is a clear literature gap regarding plastic conveyor chains. • A new physical model is introduced to describe link–rail interaction. • An extensive experimental campaign is conducted to explore the use of temperatures and vibrations. [ABSTRACT FROM AUTHOR]

• The "bilateral reduction" effect of Rényi entropy is theoretically studied and proved. • Relative entropy is extended by adding order α to generalized relative entropy. • Generalized relative entropy satisfies six sparse criteria. • Conversion between complexity measures and sparsity measures is realized. • New sparse measure cluster is proposed to enrich machine condition monitoring. Complexity measures and sparsity measures are capable of detecting and characterizing underlying dynamic changes in machine condition monitoring. Previous studies have developed Shannon entropy and Kullback-Leibler (KL) divergence and their generalization, namely Rényi entropy and Rényi divergence, but their theoretical properties as a function of sparsity and their transitions from complexity measures to sparsity measures are not fully explored and discussed. This paper continues conducting theoretical and experimental investigations on Rényi entropy and Rényi divergence and exploring their theoretical properties and transitions to enrich the domain of complexity and sparsity measures and their applications to machine condition monitoring. Specifically, this paper theoretically proves and verifies that Rényi entropy has a similar "bilateral reduction" effect exhibited by other complexity measures including correlation dimension, approximate entropy, fuzzy entropy and sample entropy. Inspired by relative entropy, which is a special case of KL divergence and a sparsity measure, and the relationship between KL divergence and Rényi divergence, in this paper, we attempt to generalize relative entropy by adding an order of α , to convert a series of complexity measures from Rényi entropy to a series of sparsity measures, which answers how to realize transitions from complexity measures to sparsity measures. More importantly, this paper is helpful to provide a guideline for correct uses of Rényi entropy, Rényi divergence, and generalized relative entropy for the quantification of impulsive transients caused by machine faults for machine condition monitoring. [ABSTRACT FROM AUTHOR]

In view of the following problems existed in the data-driven resistance spot welding (RSW) quality judgment methods: relying on expert experience, resulting in a high misjudgment rate; affected by the initial value, it is easy to fall into the local extreme point; multiple parameters needed to be modulated to increase the training time of the model. Therefore, this paper proposed a RSW quality judgment method based on revised quantum genetic algorithm (RQGA) and hidden markov model (HMM). Which used quantum rotation gate to replace the evolution process of genetic algorithm (GA), reducing the number of parameters that needed to be modulated in the model, and optimizing the initial model of HMM by RQGA to avoid falling into local extreme point. At the same time, combining expert experience with HMM to make quality judgment to ensure the accuracy of judgment. Firstly, by RSW test to obtain the main influencing factors of quality, and combining with expert experience to realize the preliminary judgment of it, on this basis, HMM was established. Next, to address the susceptibility of HMM to initial model, quantum genetic algorithm (QGA) was utilized to search for the optimal initial model. Moreover, in view of the limitations of QGA ("coding length disaster" and fixed rotation angle), a corresponding improvement was also presented. At last, the established model was applied to the RSW process of stainless steel sheets railway carriage roof. Compared with the other five models, it took the least time to complete parameter training of HMM in four quality states, less than 15s; when judging the quality, its log-likelihood probability value was −47.8418, which was close to the maximum value of this state; during the classification test, the average classification accuracy of the four quality states were 90.8 %, 92.2 %, 90.8 % and 92.0 %, respectively, which was higher than the other five models. Consequently, the model established in this paper was effective. [ABSTRACT FROM AUTHOR]

[Display omitted] • A light-weight morphing wing with multiple and smooth morphing shapes. • The dynamic model for the multi-mode morphing of the complex mechanism. • The effect of clearances, flexible links and actuation modes on the wing vibration. • The results have a crucial engineering value for the morphing wing manufacturing. Morphing wings have enjoyed much research interest due to their advantages in improving aircraft adaptability to various service environments. A single drive and continuous variable bending morphing mechanism with multiple configurations is proposed. The morphing wing designed based on this mechanism can have the advantages of lightweight, multiple configurations and smooth morphing shapes. Transient aerodynamic loads will destroy the dynamic performance of the wing during the multi-mode morphing process. Therefore, it is crucial to analyze the vibration characteristics for the reliability of the morphing wing. However, it is difficult to obtain its dynamic model and solution because of the complex configuration and the multi-mode morphing. In this paper, a mathematical model for the multi-mode morphing of the morphing mechanism based on lockable joints is described using the constraint equation firstly. Then, a rigid-flexible coupling dynamic model of the morphing wing with clearance joints is established. Finally, the influence of the infective joint and the flexible link on the vibration properties of the morphing wing is analyzed in detail. Moreover, differences in the dynamic parameters between rapid and regular actuation are discussed. The results obtained in this paper have an important engineering value for the design and manufacture of the morphing wing. [ABSTRACT FROM AUTHOR]

[Display omitted] • Authors previously developed error prediction framework for MIMO tests. • New method is developed which considers inversion error and is more precise. • Results for similar tests are improved from old method. • Broader set of tests are conducted with high accuracy. Multiple-input multiple-output (MIMO) tests are used to replicate environmental conditions on dynamic systems in a lab setting. MIMO tests allow responses at multiple locations to be replicated far better than possible with normal single input tests. However, achieving a desired response at multiple response locations is affected by various factors. Specifying test tolerances based on multiple responses (vs. on just a single input) is challenging and of great interest. The dynamic relationships between the inputs and the outputs of the system, given by the frequency response functions (FRFs), may not be perfectly characterized. Given a desired output, the FRF matrix must be inverted to obtain an input. This process has inexact solutions because the FRF matrix is usually not square and is ill-conditioned. The realization of time histories from a frequency domain input is not perfect, and the spectral content of the actual input to the system does not match what was initially sought after. Previous work has established a framework for predicting error in MIMO tests, the approximate error prediction method (AEPM), but was limited in the types of error sources that could be accounted for and the tests it could be applied to. This paper improves that method by changing to a matrix-based formulation and accounting for inversion error, called the exact error prediction method (EEPM). The EEPM is applied to similar tests in the previous paper, specifically single-input single-output (SISO) and square MIMO tests, with significant improvements in error prediction over the AEPM. Additionally, a broader set of rectangular MIMO tests, where inversion is a large source of error, are conducted with similarly effective results. [ABSTRACT FROM AUTHOR]

Joint input-state estimation algorithms such as the Augmented Kalman Filter (AKF) rely on the assumption that the distribution of the input loads follows simple spatial patterns, which are either spatially distributed or sparse. However, in many real situations the spatial distribution of the loads is complex and time-varying, which poses a significant challenge. To tackle this issue, this paper introduces a novel method named the Augmented Kalman Filter with Physics Informed Latent Force Models (AKF-PILFM), which incorporates knowledge about the physics underlying the dynamics of the input loads in the estimation based on training data. In the paper, the general formulation of the AKF-PILFM is described, followed by its tailored application to structures subjected to loads with a complex spatiotemporal behavior such as wave loads. The dominant characteristics of the wave loads are identified from training data via the Dynamic Mode Decomposition, enabling the development of a state space model of the loads that can be integrated with the AKF-PILFM. To demonstrate the effectiveness of the proposed methodology, a numerical example is provided consisting of a 3D steel jacket structure subjected to nonlinear irregular waves. The AKF-PILFM shows accurate estimation results and, in the example, outperforms an existing method based on the Gaussian Process Latent Force Models and equivalent modal loads, signifying that the load's spatiotemporal behavior substantially influences the structural response. [ABSTRACT FROM AUTHOR]

Blade tip timing (BTT) is a potential non-contact vibration measurement technique for rotating blades owing to its high efficiency and long service life. However, probe layout design and vibration parameter identification are critical challenges in BTT due to its inherent undersampling. To address this issue, we have proposed the concept of statistical BTT measurement which promotes efficient layouts and parameter identification methods by shifting the recovery target from the signal to itself to its statistics. In the first paper of series papers, we have developed covariance architecture for BTT measurement and signal post-processing. In this second part of series papers, we set out to develop BTT measurement from a higher-order statistic perspective. Specifically, we find that the 2 q th-order cumulant retains the vibration information (frequency and amplitude) of the signal itself and it can be recovered at lower sampling rates than what is prescribed by the covariance architecture. On this basis, we propose a higher-order cumulant architecture for BTT measurement, which contains two aspects: higher-order difference layout (HODL) and higher-order cumulant-based parameter identifications. HODL is a special family of layouts that physically guarantee the availability of consecutive higher-order cumulant estimations and thus leads to a satisfactory parameter identification. To facilitate application, a series of concrete HODLs are provided for users, including a four-level nested layout and its enhanced version, and optimal fourth-order difference layout. The quantitative comparison in the simulations and experiments shows the effectiveness of the proposed higher-order cumulant architecture. Compared with the architectures developed from covariance and signal itself perspective, the proposed higher-order cumulant architecture is not only more robust to noise but also further reduces the number of required probes. • Higher-order cumulant of BTT signal is used instead of the signal itself as an analysis object. • We derive a higher-order difference layout that guarantees the performance of the architecture. • A series of optimal layouts is provided for users. • A higher-order MUSIC spectrum is used for parameter identification. • We develop the statistical BTT measurement from a higher-order cumulant perspective. [ABSTRACT FROM AUTHOR]

• A BV-DL method for predicting DWRD is presented. • The real DWRD of a rail vehicle is obtained with the help of 3DCT. • A laser displacement sensor-based method is used to verify the reliability of the 3DCT model. • A traditional image processing method and two DL methods are compared with the methods proposed in this paper. Large displacements between the wheel and rail lead to track damage or even train derailment. Detecting dynamic wheel-rail displacement (DWRD) can provide a technical means for discriminating the operational health status of rail vehicles and formulating active control strategies. In this paper, a new method, which fuses binocular vision (BV) and deep learning (DL), is proposed to detect the DWRD online, the method is abbreviated as BV-DL. First, a binocular camera is used to capture the dynamic wheel-rail contact video online, while two lasers are equipped to focus on the wheel-rail contact region to locate some keypoints in the wheel-rail contact images. Then, an ROI (region of interest) detection network is proposed to recognize the wheel-rail contact region automatically, and a keypoint detection network is proposed to detect the wheel-rail pixel displacement (WRPD). Finally, a 3D coordinate transformation (3DCT) model is built to calculate the wheel-rail world displacement in real-time, i.e., the DWRD between the keypoints. To test the performance of the method, a laser displacement sensor-based method is used to verify the reliability of the 3DCT model. Meanwhile, a traditional image processing method, a MobileNet V3-based method, and a YOLOX-s-based method are introduced to predict the WRPD. Experimental results show that compared with the four methods, the proposed BV-DL method can achieve optimal results in pixel coordinate calculation and 3D coordinate computation. [ABSTRACT FROM AUTHOR]

This paper represents the final part of a sequence on the foundations of Population-Based Structural Health Monitoring (PBSHM). The previous papers presented detailed analyses of how PBSHM can extend the functionality of 'classical' single-structure SHM by leveraging information and data available across a population of structures. Two main concepts were critical in establishing the basic methodology. In the first place, it was assumed that the uplift from considering a population would only be truly present if a structure of interest could be shown to be 'similar' in some sense to at least one other structure in the population. This requirement of similarity demanded the development of a principled means of measuring the degree of said similarity; this was accomplished by the proposal and design of Irreducible Element (IE) and Attributed Graph (AG) models of structures, which naturally resided in a metric space. The second key concept in PBSHM was the idea of moving inferences between structures, and the natural methodology for this was established as transfer learning. This final paper is concerned with showing how the main ideas of PBSHM can be embedded in software; in fact, this proved to be non-trivial and not a simple matter of programming. In order to embed the concepts of PBSHM within a computational framework, a number of new concepts needed to be developed – the network , framework and database – and these ideas are introduced within the paper. Furthermore, the development of the computational architecture revealed a number of limitations inherent in the previously-proposed base PBSHM ideas, notably in the conception of the IE model. This paper therefore provides a radical reformulation of the IE-model concept, presenting it in a new form appropriate to embedding in software. The development of new theoretical constructs proved to be so entwined with the software development issues that they are not easily separable, so new concepts and software implementation are both presented here. To avoid interrupting the narrative flow of the conceptual material, the details of many of the software 'objects' are presented in a substantial appendix. • A first holistic computational conceptualisation of PBSHM. • A novel approach for embedding structural knowledge within a technical framework. • A radical reformulation of the IE-model concept. • New theoretical constructs for the development of a shared data domain within PBSHM. • Implementation of the database and framework software. [ABSTRACT FROM AUTHOR]

This paper develops an active sensing and estimation system for protecting the rider of an e-scooter from car-scooter collisions. The objective is to track the trajectories of cars behind the e-scooter and predict any real-time danger of car-scooter collision. If the danger of a collision is predicted, then a loud car-horn-like audio warning is sounded to alert the car driver to the presence of the scooter. A low-cost (∼$100) single-beam laser sensor is chosen for measuring the positions of cars behind the scooter. The sensor is mounted on a stepper motor and the region behind the scooter is scanned to detect vehicles. Once a vehicle is detected, its trajectory is tracked in real-time by using feedback control to focus the orientation of the laser sensor such as to make measurements of the right front corner of the vehicle. A nonlinear vehicle model and a nonlinear observer are used to estimate the trajectory variables of the tracked car. The estimated states are used in a receding horizon controller that controls the real-time position of the laser sensor to focus on the vehicle. The developed system is implemented on a Ninebot e-scooter platform. Extensive experiments conducted with multiple vehicle maneuvers show that the closed-loop system is able to accurately track vehicle trajectories and provide audio alerts to prevent collisions. This paper constitutes the first-ever development of active rider protection technology for the protection of e-scooters. [ABSTRACT FROM AUTHOR]

This paper explores the development of a unified hybrid approach for sensor and actuator fault diagnosis in digital twins for remote operations. Central to this approach is the implementation of a robust adaptive Kalman filter algorithm, which forms the backbone of the proposed unified algorithm. The essence of this unified algorithm lies in its capability to effectively filter the sensor measurements. The algorithm is enriched with tuning parameters, offering flexibility in adjusting the convergence rate to suit operational requirements. Noteworthy for its robustness, our approach excels in handling uncertainties and diverse types of faults, including drift, bias, noise, and freeze fault scenarios. Through comprehensive simulation and experimental evaluations conducted on a small surface vessel, the method demonstrates remarkable proficiency in accurately identifying sensor and actuator faults. This precision enables early detection and prompt mitigation of anomalies, contributing to heightened operational resilience. [Display omitted] • This paper introduces a unified hybrid approach designed for diagnosis of sensor and actuator faults in digital twins. • The proposed algorithm relies on a modified adaptive Kalman filter as its foundational framework. • The efficacy of these algorithms is assessed through testing in the context of remote operations for surface vessels. [ABSTRACT FROM AUTHOR]

Data-driven modal analysis is an indispensable means to understand the dynamic behavior of engineered structures and natural systems. It effectively captures active dynamic behavior and embeds them in a set of identified linear modal subspaces, unveiling complex dynamic behavior via the superposition of simpler hierarchical spatiotemporal dynamics. Despite the existence of many methods, there is no unified perspective connecting these modal analysis techniques. This paper proposes a new modal decomposition method, the characteristic value decomposition (CVD), that provides a unifying paradigm for data-driven modal analysis. CVD identifies identical spatial modes and temporal coordinates from two measurement data matrices. The modal parameters are identified through an eigendecomposition to a quotient of auto-covariance and cross-covariance matrices of the two data matrices. This paper shows that several data-driven modal analysis methods, namely, the state-variable decomposition, the dynamic mode decomposition, the Ibrahim time domain method, and the eigensystem realization algorithm, are special cases of CVD when the two data matrices are appropriately selected. Moreover, the CVD identifies the modal parameters with higher accuracy and is robust to added noise compared to established methods, as demonstrated using numerical experiments. It is also shown that the CVD flexibly utilizes data from multiple experiments and undersampled data sets to unveil system dynamics. [Display omitted] • Characteristic value decomposition (CVD) is a unifying paradigm for modal analysis. • CVD simultaneously decomposes two data sets into shared modes and modal coordinates. • CVD physically finds balanced and dynamic direction-invariant modal parameters. • CVD provides more accurate modal parameter estimates and is robust to noise. • CVD is flexible for ensemble decomposition and is adaptable to undersampled data. [ABSTRACT FROM AUTHOR]

Blind deconvolution (BD) has been demonstrated to be an efficacious approach for extracting bearing fault-specific features from vibration signals under strong background noise. Despite BD's appealing feature in adaptability and mathematical interpretability, a significant challenge persists: How to effectively integrate BD with fault-diagnosing classifiers? This issue is intricate to be tackled because the traditional BD method is solely designed for feature extraction with its own optimizer and objective function. When BD is combined with the downstream deep learning classifier, the different learning objectives easily get in conflict. To address this problem, this paper introduces classifier-guided BD (ClassBD) for joint learning of BD-based feature extraction and deep learning-based fault diagnosis. Towards this goal, we first develop a time and frequency neural BD that employs neural networks to implement conventional BD, thereby facilitating seamless integration of BD and the deep learning classifier for co-optimization of model parameters. In the neural BD, we incorporate two filters: (i) a time domain quadratic filter to utilize quadratic convolutional networks for extracting periodic impulses; (ii) a frequency domain linear filter composed of a fully-connected neural network to amplify discrete frequency components. Next, we develop a unified framework built upon a deep learning classifier to guide the learning of BD filters. In addition, we devise a physics-informed loss function composed of kurtosis, l 2 / l 4 norm, and a cross-entropy loss to jointly optimize the BD filters and deep learning classifier. In so doing, the fault labels are fully exploited to direct BD to extract features in distinguishing classes amidst strong noise. To the best of our knowledge, this is the first of its kind that BD is successfully applied to bearing fault diagnosis. Experimental results from three different datasets highlight that ClassBD outperforms other state-of-the-art methods under noisy conditions. The source codes of this paper are available at https://github.com/asdvfghg/ClassBD. [ABSTRACT FROM AUTHOR]

• New type of hybrid gas foil thrust bearing with spiral groove on the top foil surface is proposed. • The effects of spiral groove structure (number of grooves, groove depth, groove width ratio) on the static characteristics of the new type of hybrid gas foil spiral groove thrust bearing at 15krpm and 20krpm respectively is researched by experiment. • Design and machining of gas foil spiral groove thrust bearings with different groove structure parameters. • The gas foil spiral groove thrust bearing test rig was set up to collect data on bearing load capacity, frictional power loss, bearing stiffness, and other bearing characteristics using a data acquisition system. • Optimal spiral groove structure parameters were obtained. • The GFSGTB load capacity quick calculation equation is obtained by fitting according to the test results. Traditional gas foil thrust bearings often face the challenge of inadequate load capacity when under high load conditions and cannot be used in high power and heavy load turbomachinery with large external axial forces. Aiming to enhance the bearing load capability, this paper proposes a novel hybrid gas foil spiral groove thrust bearing (GFSGTB), which retains the advantages of simple structure of gas foil thrust bearing and uses the spiral groove on the top foil surface as an auxiliary pressurization mechanism of the bearing, so that the bearing load capability can be further improved by using the pumping effect and circumferential ladder effect of the spiral groove on the basis of taper dynamic pressure effect. In addition, the gas foil spiral groove thrust bearing with different spiral groove structures were designed and a bearing performance test rig was built to measure the load capability, bearing stiffness and frictional power loss of the gas foil spiral groove thrust bearing. The research in this paper focuses on the static characteristics of bearings. The test results indicate that: the load capability of gas foil spiral groove thrust bearings at N = 12 is increased by 76.84 % and the frictional power loss is reduced by 65.26 % compared with that of non-groove gas foil thrust bearings. When groove width ratio B g = 0.051, the bearing load capability raised by 93.09 % and the frictional power loss decreases by 70.08 %. When groove depth h g = 30 μm, the bearing load capability is raised by 82.57 % and the frictional power loss is decreases by 75.73 %. Additionally, the GFSGTB load capacity quick calculation equation is obtained by fitting according to the test results in this paper, which can quickly and accurately get the bearing load capacity of GFSGTB, and the error is less than 10 %. The results of this study can provide suggestions for significantly increasing the load capacity of gas foil thrust bearings. [ABSTRACT FROM AUTHOR]

In recent years, the response-controlled stepped-sine testing (RCT) method has made important contributions to the evolution of the single nonlinear mode (SNM) method from a postulate to a well-established theory with the accurate modal identification of various nonlinear mechanical systems ranging from simple benchmark beams to real missiles. Since the RCT method resides on the SNM theory which is based on Rosenberg's periodic motion definition of nonlinear modes, it has been regarded as limited to weakly damped mechanical systems that mainly exhibit strong conservative nonlinearities. An important contribution of this current paper is extending the RCT method to non-conservative mechanical systems with complex nonlinear modes. The extension is first numerically validated on a strongly friction-damped cantilever beam and then experimentally validated on the actuation mechanism of a real control fin that exhibits strong softening-stiffening nonlinearity (up to 50% shift of natural frequency) due to friction/backlash and very high and nonlinear damping (up to 30% modal damping ratio) due to friction. Another important contribution of this paper is to demonstrate the applicability of the superposition principle in nonlinear dissipative systems with closely spaced modes at an acceptable accuracy if the nonlinear modes are similar in shape to their corresponding linear modes and the modal coupling terms in the equation of motion can be ignored. The assumption of similar nonlinear modes is reasonable for most practical applications that exhibit dissipative nonlinearities due to mechanical joints. Although the assumption of ignorable coupling terms does not reside on a solid theoretical framework, numerical studies on a friction-damped lumped system with closely spaced modes give promising results, pointing to the significant potential of the modal superposition in nonlinear systems. [ABSTRACT FROM AUTHOR]

Arc stability determines welding quality, so it is important to study arc stability online detection. In order to solve the problem, a rotating arc stability sensor is developed in this paper, which can realize the online detection of arc stability. Firstly, the general model of the input and output mapping relationship of the rotating arc stability sensor and its parameter determination method have been studied. Then, the parameter adaptive mathematical model establishment method based on the objective optimal neural network has been studied, and the parameter adaptive model of the rotating arc stability sensor has been established based on the objective optimal neural network, so that the parameters of the model can be automatically adjusted online according to the uncertain and changing welding state. Finally, the online detection method of arc stability has been studied based on the parameter adaptive model of the rotating arc stability sensor. Using the method designed in this paper, arc stability detection experiments were conducted under different welding current, welding voltage, and weld shape conditions. The experimental results showed that the detected arc stability and welding deviation were consistent with the actual welding results. Therefore, the method designed in this paper can accurately and reliably detect the arc stability. [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]

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]

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]

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]

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]

• No input knowledge: Concentrates solely on accelerometer responses. • Challenges: Noise, turbulence, non-optimal excitation, sensor issues. • Time-delay Dynamic Mode Decomposition + Robust PCA + Sparsity-promoting criterion. • Enhanced with compressed sensing to work effectively with limited sensor measurements. • Highly suitable for real-time use in flutter flight test campaigns. Flutter flight test involves the evaluation of the airframe's aeroelastic stability by applying artificial excitation on the aircraft lifting surfaces. The subsequent responses are captured and analyzed to extract the frequencies and damping characteristics of the system. However, noise contamination, turbulence, non-optimal excitation of modes, and sensor malfunction in one or more sensors make it time-consuming and corrupt the extraction process. In order to expedite the process of identifying and analyzing aeroelastic modes, this study implements a time-delay embedded Dynamic Mode Decomposition technique. This approach is complemented by Robust Principal Component Analysis methodology, and a sparsity promoting criterion which enables the automatic and optimal selection of sparse modes. The anonymized flutter flight test data, provided by the fifth author of this research paper, is utilized in this implementation. The methodology assumes no knowledge of the input excitation, only deals with the responses captured by accelerometer channels, and rapidly identifies the aeroelastic modes. By incorporating a compressed sensing algorithm, the methodology gains the ability to identify aeroelastic modes, even when the number of available sensors is limited. This augmentation greatly enhances the methodology's robustness and effectiveness, making it an excellent choice for real-time implementation during flutter test campaigns. [ABSTRACT FROM AUTHOR]

• New phenomena of unreported voltage islands and broad-frequency constant voltages are revealed. • Appropriate parameters of k 1 induce multiple voltage islands, enhancing energy harvesting efficiency. • Novel broad-frequency constant voltage phenomenon enriches existing insights into energy harvesting. • The energy harvester converts vibrations into electricity, powering LEDs for emergency lighting and lab signage. This paper investigates a nonlinear piezoelectric energy harvesting system that enhances conversion of mechanical energy to electrical energy by integrating piezoelectric materials with a nonlinear system. The research simplifies a flexible beam with macro fiber composite (MFC) piezoelectric patches using a lumped-parameter approach and derives the dynamic equations of the system using the Lagrange equations. The incremental harmonic balance method (IHBM) is then employed to solve these equations, simulating the nonlinear dynamics involved in the harvested voltage. Key findings include the initial discovery of multiple voltage islands which are voltage jumps that improve energy conversion efficiency, confirmed by experimental data validating the IHBM simulations. The study reveals that these phenomena are due to the enhanced nonlinearities of the system, particularly the geometric nonlinearity caused by the spring k 1 and the nonlinear magnetic force between magnets. Adjusting k 1 allows the observation of multiple voltage islands, and increasing d 0 results in a broad-frequency constant voltage, offering new insights. The research concludes that the nonlinear piezoelectric energy harvester can effectively convert vibration energy into electrical one, as demonstrated by successfully powering multiple light-emitting diodes (LEDs). Bifurcation diagrams illustrate the adaptability of the system, showing transitions among periodic, quasi-periodic, and chaotic states for different parameters. [ABSTRACT FROM AUTHOR]

Pneumatic vibration isolators play an increasingly important role in precision manufacturing. In this paper, a H∞ active control strategy for pneumatic vibration isolation system based on voltage input saturation is proposed and studied. The objective is to suppress the resonance peaks and low-frequency vibration disturbances near the intrinsic frequency of the vibration isolation system. The control algorithm is highly robust to system model errors, and frequency characteristic shaping design is performed by selecting appropriate weighting functions according to the performance objectives to achieve maximum vibration suppression in the frequency band where active control is required. Considering the smoothness of the operation of the pneumatic vibration isolation system and the safety of the control channel, input saturation control of the input voltage is carried out. Finally, a 1–10 Hz excitation signal is applied to test the performance of the pneumatic vibration isolation system under this active control method, and the experimental results show the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

[Display omitted] Currently, dielectric elastomer generators (DEGs) demonstrate high energy density, rapid response, and simple structure in the field of energy harvesting (EH). However, the efficient multi-directional dielectric EH in complex vibrational environments, particularly at ultra-low frequencies (0–3 Hz), remains a significant challenge. To solve this issue, a pendulum-type annular DEG (PA-DEG) is designed in this paper to harvest multi-directional ultra-low-frequency vibrational energy. The PA-DEG consists of an annular dielectric elastomer membrane (DEM) that can be deformed by a pendulum structure, which swings under multi-directional vibrations. The dynamics model of the proposed PA-DEG under multi-directional vibrational excitations is established and verified through experiments. The PA-DEG under vibrational excitations exhibits rich dynamical behaviors resulting from the DEM's resonance, the pendulum's response and the system's 1:2 internal resonance, which are revealed through numerical simulations. Moreover, the electrical model of the PA-DEG is established to study its potential for ultra-low-frequency EH under different vibration directions. It is found that the PA-DEG can harvest ultra-low-frequency vibrational energy, whose EH performance is significantly influenced by the excitation parameters and the structural parameters. A comparative study between the PA-DEG and the system without a pendulum swing is further conducted under swept frequency excitation. Research results show that the PA-DEG can achieve higher output power within an ultra-low-frequency range with the help of the pendulum, thus demonstrating its superiority in high-performance ultra-low-frequency multi-directional vibration energy harvesting. [ABSTRACT FROM AUTHOR]

Time-frequency analysis methods is an effective tool to analyze non-stationary signals. Moreover, the utilization of postprocessing algorithms significantly enhances this analytical capability. However, these methods have certain limitations when dealing with non-stationary signals with strong time-varying laws. We put forward an adaptive linear chirplet synchroextracting transform (ALCSET) based on chirplet transform (CT) to deal with this problem. This paper first optimizes the CT by measuring Gini index to generate a time–frequency representation with accurate amplitude. Then, an improved synchroextracting operator is employed to obtain high-resolution and energy concentration time–frequency representation. The simulation experiments of non-stationary signals demonstrate that the significant advantages of the proposed method in terms of energy aggregation, noise robustness, and signal reconstruction. Furthermore, the practical value of the method is verified by the experimental signal. [ABSTRACT FROM AUTHOR]

• A 2DOF nonlinear electromagnetic energy harvester using magnetic levitation architectures in rotational motion is explored. • A comprehensive theoretical model with the stribeck friction in rotational motion is established. • The influence of the centrifugal force on broadening the operating frequency band is investigated. • The maximum average output power reaches 4.6 mW in experiments. Electromagnetic energy harvesting has sparked the interest of researchers due to its promising potential in powering miniaturized electronic systems and wireless sensor networks. This paper focuses on a two-degree-of-freedom nonlinear electromagnetic energy harvester (2DOF-NEMEH) using a magnetic levitation architecture in rotational motion. The residual magnetic flux density is identified by the finite element method and gradient descent method, enabling a more precise description of the model. A dynamic model, accounting for the centrifugal force and the Stribeck friction in rotational motion, is established and validated by an agreement between experimental and numerical results. The mechanisms that can respectively enhance the energy output performance of the 2DOF-NEMEH in two different installation configurations are studied: One is to enhance the maximum output power through the negative damping of the stick–slip phenomenon for low-speed rotational motion, while the other is to broaden the operating frequency band through the combined effect of centrifugal force and 2DOF arrangement. The centrifugal stiffening effect and sudden-drop phenomenon are explored. Ultimately, optimization of the 2DOF-NEMEH achieved a maximum average output power of 4.6 mW and a wide operating frequency band spanning 0 to 600 rpm. Power supply tests for sensors are conducted on the optimized 2DOF-NEMEH, confirming its application potential. [ABSTRACT FROM AUTHOR]

[Display omitted] • Using Shannon wavelet transform to extract AE narrowband signal. • A dimension reduction method for near-field AE source localization is proposed. • An AE source localization method in nonuniform linear array is proposed. • The proposed method can increase the detection distance of near-field AE source. To expand the detectable range of the linear array in the near-field acoustic emission source localization process to improve the localization accuracy and efficiency, this paper proposes a method of near-field acoustic emission source localization based on orthogonal matching pursuit under nonuniform linear array. Firstly, according to the propagation characteristics of acoustic emission signals, a narrowband signal decomposition method for broadband acoustic emission signals is proposed. Then, a near-field source dimension reduction method based on fourth-order cumulants under nonuniform array is proposed to realize the separation and estimation of source angle and distance parameters. Subsequently, an acoustic emission source localization method based on orthogonal matching pursuit is proposed to maximize the source localization performance of the nonuniform linear array. Finally, the performance comparison between three different nonuniform linear arrays and different methods is carried out. Meantime, the finite element simulation and acoustic emission localization experiments are used to analyze the localization accuracy law and verify the effectiveness of the method. [ABSTRACT FROM AUTHOR]

[Display omitted] • A purely center-rotation stick–slip stage with force magnification is proposed. • Single piezoelectric actuator avoids driving redundancy. • Built-in driving mechanism ensures structural compactness. • Dynamics model with electromechanical, rigid-flexible, and friction coupling. • A series of experimental validations. This paper proposes a purely center-rotation and non-redundant piezoelectric stick–slip rotary stage with force magnification. A bridge driving mechanism with centrosymmetric actuation feet is proposed, and its tilt angle can be adjusted to regulate the clamping force amplification characteristics, thus improving load capacities. Meanwhile, only a single piezoelectric actuator is used, and the driving mechanism is built in the circular slider, hence achieving non-redundant, purely centered rotation and improving the structural compactness. Theoretical calculations and simulation analyses of single-step rotation angles and force amplification of the rotary stage are carried out. In addition, a system dynamics model with electromechanical, rigid-flexible, and friction coupling properties is established. Finally, a prototype is fabricated, and various experiments are conducted. The designed bridge driving mechanism amplifies the piezoelectric actuation force by 2.8 times, and the stick–slip rotary stage has a maximum load of up to 9 kg. Also, the stage can rotate more than 360°, and the angular resolution is 0.1265 μrad, demonstrating superior performances of the non-redundant center-rotation stage. [ABSTRACT FROM AUTHOR]

In the stability analysis of robotic milling processes, two primary chatter mechanisms have been identified — regenerative chatter and mode-coupling chatter. They are known to result in undesirable outcomes such as poor surface finish, dimensional errors, and reduced lifespan of tools and machines. Recently, there is increasing disagreement regarding the existence of mode-coupling chatter. Specifically, Mode-coupling chatter has been suspected from the perspective of Stability Lobe Diagrams (SLDs) because of the mismatch between experiments with the predicted SLDs obtained by the mode-coupling mechanism, especially the mismatch between the two in terms of spindle speed dependency on stability boundary. Nonetheless, the opinion has not been fully recognized in robotic milling. Therefore, to investigate the existence of mode-coupling chatter further from the perspective of signal processing, this paper proposes a novel method for determining chatter mechanisms based on two chatter type indicators (CTIs) comprised of the number of true modes (NTM) and the distribution of characteristic multipliers (DCM). Through the simulation using Mathieu equation, the NTM and DCM are obtained by dynamic mode decomposition (DMD) and proved to be effective in distinguishing the response signals of mode-coupling chatter from regenerative chatter. Through robotic milling experiments and Spearman correlation analysis, the mode-coupling chatter is deduced not to happen in robotic milling. [ABSTRACT FROM AUTHOR]

Although the series–parallel hybrid welding manipulator has advantages over the traditional series manipulator, its must run continuously and smoothly according to a specific trajectory at the rated velocity. Considering a flexible manipulator with a complex series–parallel hybrid structure as the research object, this paper proposes a multi-objective approach to indirectly suppress the vibration. In view of the complex flexible mechanism with a multi-kinematic pair arrangement and a multi-motor drive, a system comprehensive elastodynamics model is established, and the effectiveness of the model is verified through the hammer test method. To achieve synchronous and coordinated planning of the end position and attitude, the spatial arc welding trajectory is planned on the basis of the synchronous planning controller and the dual-S velocity profile interpolation function. Then, a multi-objective optimization model considering the travel time, energy consumption and terminal amplitude fluctuations is proposed, the trajectory planning problem is transformed into a parameter optimization problem in the interpolation function, and then the elite non-dominated sorting genetic algorithm (NSGA-II) is employed to determine the optimal trajectory. The optimal trajectory thus obtained effectively suppresses the vibration of the system. Finally, compared with the classic fifth-degree polynomial trajectory planning method, the system vibration suppression effect is significant, which verifies the effectiveness and feasibility of the proposed method and provides a theoretical basis for further realizing real-time control. • Proposes a multi-objective approach to indirectly suppress the vibration. • Introduce the dual-S speed profile interpolation function and the synchronous planning controller. • Achieve synchronous and coordinated planning of the end position and attitude. • Establish a comprehensive elastodynamics model of the series–parallel hybrid system. • The system vibration suppression effect is significant compare with the classic fifth-degree polynomial trajectory planning method. [ABSTRACT FROM AUTHOR]

The hydrodynamic performance, supporting characteristics of a misaligned water-lubricated bearing are investigated. The rotor-dynamic behaviors of bearing-rotor system are explored. The effects of operation parameters on static performances, dynamic properties and response analysis of such strong coupling system are revealed. The water-lubricated bearing-rotor test bench is designed and constructed. The Misaligned water-lubricated bearings with different structures and geometries are manufactured. The tribo-dynamics and vibration analysis of the bearing are carried out. The procedure is proved to be effective through the test data. [Display omitted] • The revised lubrication-rotordynamic model incorporating misalignment is built. • The impacts of the deflection on physical performances are systematically discussed. • The changing rules of the inclination effect on the rotor-dynamic are analyzed. • The correctness and rationality of the model is verified by experiment. The strong time-varying dynamic load, irregular and unpredictable impact of the propeller lead to the shaft inclination and uncertain contact, which worsen the stability of propulsion system. The paper studies the rotor dynamics of the system with bi-misaligned effect on the novel bearing lubricated by water. Researches indicate that misalignment has the stiffness-hardening effect on the main stiffness coefficient. It has stiffness-weakening effect on the cross-stiffness coefficient. Horizontal inclination has significant impact on the vibration phase of the shaft while vertical inclination influences the amplitude remarkably. The impact of bi-directional bias on vibration is combination of horizontal and vertical influences. The bi-direction inclination balances the effect of unidirectional vibrations. Results provide solid foundation for cutting-edge exploration and optimum design for such bearing systems. [ABSTRACT FROM AUTHOR]

This paper explores the significant impact of thermal effects on the performance of Swashplate-Type Axial Piston Machines, a crucial aspect often overlooked in current numerical analyses primarily focused on energy efficiency prediction and machine design optimization. Existing methods commonly neglect or only partially account for thermal dynamics and heat transfer within both fluid and solid domains, resulting in inaccurate power loss predictions and hindering designers' ability to enhance energy efficiency and durability. To address this gap, we present a comprehensive thermal model that fully couples the thermal behavior of the entire machine. This model incorporates mass and energy conservation in displacement chambers and slipper pockets, dynamics of floating bodies, Reynolds equation, and energy conservation in lubricating interfaces. Furthermore, it considers heat transfer in floating bodies and thermo-elasto-hydrodynamic effects induced by pressure and thermal deformation. Validation against experimental data demonstrates the accuracy of the model in predicting solid body temperature distribution and overall machine efficiency. By comparing machine efficiencies with an isothermal model, we highlight the critical importance of considering thermal effects. Additionally, our model offers insights into key power loss sources across various operating conditions, contributing to a deeper understanding of machine performance and aiding in future design optimizations. [ABSTRACT FROM AUTHOR]

The detection of internal defects within railway tracks stands as a critical aspect of ensuring transport security, given its potential to precipitate severe accidents. Phased array ultrasonic testing methods have gained extensive usage in rail inspection owing to their remarkable precision and expansive coverage. Traditional ultrasonic inspection techniques typically require either a fixed relative position between the transducer and the specimens or a gentle movement of the transducer to beamform. However, the unavoidable vibration stemming from the vehicle–rail coupled interactions often results in time-variant media, significantly impacting the beamforming. This paper presents a refined wavenumber-domain plane wave imaging technique tailored to enable real-time dynamic imaging, specifically accommodating the vertical vibrational movement of the transducer. Real-time calculation of the transmission focal law is achieved by numerical fitting. The wavenumber-domain technique is utilized to realize real-time accurately reconstructed images. The efficacy of the proposed method is assessed through simulations and experiments performed on a convex cylindrical specimen and a rail head sample, respectively. It can be seen that this approach can achieve a frame rate of 83.3 fps with a pipeline architecture in this study, demonstrating the capability to reconstruct images in vibrating environments, such as during the high-speed inspection of railways. • Focal laws are dynamically adjusted for probe vibration in rail inspection. • Real-time frame rates of up to 83.3 fps are achieved for time-varying media. • High accuracy inspection results are demonstrated in vibration environments. [ABSTRACT FROM AUTHOR]