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Blade cracks pose deadly threats to aviation safety and have caused serious aviation accidents in recent years. In order to diagnose blade cracks at the early stage, the harmonic vibration of cracked blade was analyzed theoretically and experimentally. Firstly, a recursive form solution was deduced for the complex the nonlinear dynamic equation of blade vibration, revealing the close relationship between harmonic component power and crack parameters. Furthermore, the upper bound of the adjacent harmonic component power ratio was obtained by theoretical derivations. The results show that the harmonic power decreases as the harmonic component order increases, and the degree of attenuation is decided by the crack parameters. Therefore, a crack detection approach was proposed according to the power ratio of harmonic components. The advantage of this method is that the blades do not need to be in a resonant state and can process vibration data at all rotational frequencies. This improves data utilization and diagnostic robustness. The recommended method was validated by the simulation analysis of stainless-steel and titanium blades. Finally, a test bench for blade vibration was set up whose highlight was the use of optical sensors for non-contact measurements of blade vibration. The results of both simulation and testbed experiment were much consistent with the theoretical inference. • Although a closed-form solution to the nonlinear kinetic equation of blade vibration is theoretically not available, we obtain a solution in a recursive form. • Further, the energy attenuation function of adjacent harmonic components is defined, and it is theoretically derived that there is an upper bound of the energy attenuation function. Moreover, the upper bound of the energy attenuation function is determined by the harmonic order, crack location, and crack depth. • The results of simulation and test bench experiments are consistent with theoretical inferences. [ABSTRACT FROM AUTHOR]

Acoustic emission (AE) signals are useful for the condition monitoring of mechanical seals as tribological regimes affect the AE signatures. In this paper the investigation develops a mathematical model that can predict the energy of an AE signal under different tribological regimes. The developed model has been validated with experimental studies and satisfactory results have been perceived. Therefore, the model has strong potential to be used to obtain tribological behaviour of mechanical seals and hence develop a reliable and accurate condition monitoring system under varying operating conditions. [ABSTRACT FROM AUTHOR]

Abstract: Friction stir welding is a relatively new solid-state joining technique which is widely adopted in different industry fields to join different metallic alloys that are hard to weld by conventional fusion welding. Friction stir welding is a highly complex process comprising several highly coupled physical phenomena. The complex geometry of some kinds of joints and their three dimensional nature make it difficult to develop an overall system of governing equations for theoretical analyzing the behavior of the friction stir welded joints. The experiments are often time consuming and costly. To overcome these problems, numerical analysis has frequently been used since the 2000s. This paper reviews the latest developments in the numerical analysis of friction stir welding processes, microstructures of friction stir welded joints and the properties of friction stir welded structures. Some important numerical issues such as materials flow modeling, meshing procedure and failure criteria are discussed. Numerical analysis of friction stir welding will allow many different welding processes to be simulated in order to understand the effects of changes in different system parameters before physical testing, which would be time-consuming or prohibitively expensive in practice. The main methods used in numerical analysis of friction stir welding are discussed and illustrated with brief case studies. In addition, several important key problems and issues remain to be addressed about the numerical analysis of friction stir welding and opportunities for further research are identified. [Copyright &y& Elsevier]

Abstract: Acoustic emission analysis is an effective diagnostic method in modern tribo-monitoring. Many experimental studies have been conducted to investigate the relationship between AE parameters and wear and friction. This paper develops a theoretical model to correlate acoustic emissions to sliding friction based on elastic asperity contact of materials. It is found that sliding speed, load supported by asperities, the number of asperity contact and surface topographic characteristics influence the energy of AE signal. With the help of this model it is possible to estimate the contact load supported by asperities from AE measurement, which is a critical parameter to evaluate lubrication conditions in engineering applications. The establishment of this model will provide a theoretical basis for future studies on the motioning and prognostics of tribological process using AE technology. [Copyright &y& Elsevier]

Due to excessive service load, inappropriate operating conditions or simply end of life fatigue, damage can occur in gears. When a fault, either distributed or localised, is incurred by gears, the stiffness and consequently vibration characteristics of the damaged tooth will change. A possible non-destructive technique for damage detection and severity assessment can be derived from vibration analysis. This paper presents the use of vibration analysis in the detection, quantification, and advancement monitoring of damage incurred by spur gear teeth. The stiffness of a single spur gear tooth is analysed theoretically, and due to the difficulties in measuring the gear tooth stiffness, an experimental procedure based on the modal analysis is developed to assess the severity of the gear tooth damage. A pair of spur gears was tested under accelerated wear conditions, and conventional time and frequency domain techniques are applied to the gear vibrations to indicate the presence and progression of the wear. The developed modal stiffness assessment technique is then used to quantify the resulting wear damage to the spur gear teeth. [Copyright &y& Elsevier]

Graphical abstract Abstract The self-piercing riveting (SPR) of 1420 aluminium–lithium alloy sheets is investigated. Static tests with lap-shear loads and fatigue tests using the tension–tension loading mode were applied to obtain the joint performances and failure modes. The mechanism analysis regarding failure and fretting was employed using a scanning electron microscope (SEM) and an energy dispersive X-ray spectroscopy (EDX). The 1420 Al–Li alloy sheets can be joined effectively using SPR. The fatigue load–fatigue life curve with a 95% confidence level is obtained using a variation coefficient method. The joints failed in the rivet pulled out from the locked sheet in static trials, and the locked sheet fractured during the fatigue tests. The fretting wear can be observed at the contact interface between the rivet tail and the locked sheet or between both sheets at different load levels. The fretting region changes at a high or low load level, and it is highly relevant to the fatigue rupture paths. [ABSTRACT FROM AUTHOR]

Abstract This article introduces a revised common trend framework to monitor nonstationary and dynamic trends in industrial processes and shows needs for each improvement on the basis of three application studies. These improvements relate to (i) the extension of the common trend framework to include sets that contain stationary and nonstationary variables, (ii) handling cases where residuals are not drawn from multivariate normal distributions and (iii) the application of the framework to larger variable sets. Existing work does not adequately address these practically important issues. Industrial application studies highlight the needs for (i) the extended framework to model data sets containing stationary and nonstationary variables, (ii) handling statistics that are not based on normally distributed residuals and (iii) the use of Chigira procedure to robustly extract common trends. The extended framework is compared to traditional approaches. [ABSTRACT FROM AUTHOR]

Gear tooth surface wear is a common failure mode. It occurs over relatively long periods of service nonetheless, it degrades operating efficiency and leads to other major failures such as excessive tooth removal and catastrophic breakage. To develop accurate wear detection and diagnosis approaches at the early phase of the wear, this paper examines the gear dynamic responses from both experimental and numerical studies with increasing extents of wear on tooth contact surfaces. An experimental test facility comprising of a back-to-back two-stage helical gearbox arrangement was used in a run-to-failure test, in which variable sinusoidal and step increment loads along with variable speeds were applied and gear wear was allowed to progress naturally. A comprehensive dynamic model was also developed to study the influence of surface wear on gear dynamic response, with the inclusion of time-varying stiffness and tooth friction based on elasto-hydrodynamic lubrication (EHL) principles. The model consists of an 18 degree of freedom (DOF) vibration system, which includes the effects of the supporting bearings, driving motor and loading system. It also couples the transverse and torsional motions resulting from time-varying friction forces, time varying mesh stiffness and the excitation of different wear severities. Vibration signatures due to tooth wear severity and frictional excitations were acquired for the parameter determination and the validation of the model with the experimental results. The experimental test and numerical model results show clearly correlated behaviour, over different gear sizes and geometries. The spectral peaks at the meshing frequency components along with their sidebands were used to examine the response patterns due to wear. The paper concludes that the mesh vibration amplitudes of the second and third harmonics as well as the sideband components increase considerably with the extent of wear and hence these can be used as effective features for fault detection and diagnosis. [ABSTRACT FROM AUTHOR]

Skidding is an inevitable phenomenon in rolling element bearing but accelerates the bearing wear and shortens the bearing life. Recognizing skidding behavior under different clearances will benefit to bearing health monitoring. In this study, an improved bearing dynamic model is developed to investigate its vibration characteristics and skidding behavior taking into all bearing structure components, time-varying oil film characteristics and bearing clearances. A novel method is proposed to include time-varying oil film stiffness and thickness under different clearances into the bearing dynamic model. A fast algorithm is adopted through interpolation during the model solving process. Furthermore, vibration responses and skidding behavior under different clearances are analyzed. Finally, a designed synchronous differential encoder (SDE) system tests the skidding behavior of bearings and verifies the proposed model through measuring the angular velocity of cage transformed by distance test. Numerical result shows that both RMS and ball pass frequency of outer race (BPFO) increase with bearing clearances. The slip ratio represents growth with the increase of bearing clearances, which is verified by the designed experiment with good consistency with the simulation. It can be foreseen that this novel dynamic model with SDE system can be well performed for bearing skidding detection and fault detection. [ABSTRACT FROM AUTHOR]

Bearing waviness is a kind of geometric unevenness on the surface of bearing components, which has vital influence on lifetime, vibration and noise. In order to accurately evaluate the impact of waviness on bearing operating performance and diagnostic features, it is necessary to reveal the mapping relationship between waviness excitation and vibration characteristics. However, current simulated waviness when modeling bearing vibrations is usually simplified by a uniform or non-uniform sinusoidal function, which cannot characterize the real topography of bearing waviness and lead to inaccuracy of diagnostics and prognostics. To address this issue, a novel waviness characterizing method based on Gaussian filter is developed in this study. Based on the proposed waviness characterizing method, a bearing waviness dynamic model is developed and vibration responses under various amplitude and order of waviness are investigated by simulation and experiment. Results show that the established waviness characterizing method can generate waviness curves closer to the actual shape. The bearing waviness dynamic model and the based vibration responses reveals unusual random phenomenon due to different waviness effects. These findings provide theoretical support for accurate identification of waviness on vibration characteristics, which has great significance on condition monitoring and fault diagnosis of bearing. [ABSTRACT FROM AUTHOR]

• Proposed a semantic construction method using adaptive resonance demodulation, which improves the interpretability of semantic features. • Proposed a semantic-induced zero-shot diagnosis framework to identify unknown compound faults using single fault samples of railway bearings. For the challenges of diverse compound faults and low identification accuracy of railway bearings, a new zero-shot diagnosis model based on adaptive resonance demodulation semantic is proposed for the compound fault diagnosis of railway bearings. The model adopts adaptive resonance demodulation to identify the optimal resonance frequency band rich in fault information in bearing signals, and constructs the single and compound fault semantics of samples without separating the compound fault signals, thus improving the interpretability of semantic features. Moreover, spatial Euclidean distance is used to measure the similarity of features and semantics in the mapping space, which enables the identification of unknown compound faults by single faults. Verification through railway bearing data shows that this model effectively improves the compound fault identification accuracy under zero samples and is better than the comparison models. The research results can provide theoretical reference for the research and application of railway bearing fault diagnosis. [ABSTRACT FROM AUTHOR]

• A novel method for pressure prediction and cavitation evaluation of water jacket in engines is proposed. • The pressure amplification factor for a thin liquid layer under modal vibration is derived. • The nonlinear relationship between liner vibration and coolant pressure is investigated to reveal the mechanism of vibration-induced cavitation. • Effects of acoustic parameters and liner modal parameters on cavitation are discussed. • The correctness and rationality of the method is verified by experiment. Vibrations of the cylinder liner in internal combustion engines cause coolant cavitation, inducing cavitation erosion. Owing to the lack of a comprehensive understanding of vibration-induced cavitation, the cavitation erosion has been long an unresolved problem influencing engine lifetime and reliability and concerned more in recent years due to lightweight designs. This study proposes a novel pressure prediction method that considers the dynamic properties of a cylinder liner and the pressure amplification effect of a thin water jacket. A pressure amplification factor for a thin water jacket, defined as the ratio of the acoustic pressure to the plane progressive wave pressure generated by the same liner vibration, was derived for the first time. The pressure amplification mechanism in the thin water jackets was revealed. The cavitation risk area on the cylinder liner was predicted, and the effects of acoustic parameters and liner modal properties on cavitation were analysed. The theoretical analyses agreed with the experimental results, proving the accuracy of the proposed method. Moreover, it has found that the thin-layer configuration of the water jacket significantly reduced the vibration threshold for triggering cavitation. Vibration-induced cavitation in cylinder liners is closely related to the liner constraint modes and acoustic properties of the water jackets. The research results enrich the theoretical understanding of vibration-induced cavitation and provide theoretical support for the prediction and mitigation of cavitation erosion in internal combustion engines. [Display omitted] [ABSTRACT FROM AUTHOR]

Intelligent transportation necessitates advanced perception and cognitive systems that can provide continuous feedback from the vehicle. However, sensors relying on batteries face challenges such as high maintenance costs and environmental issues due to the limited lifespan of the power source. To overcome these challenges, this paper reports an efficient battery-free solution for transportation monitoring. The solution utilizes a speed-amplified rotary energy harvester (SAREH) to power various wireless Bluetooth sensors, enabling continuous monitoring of the vehicle's motion state. The SAREH combines a contra-rotating mechanism with a friction pendulum, resulting in excellent power output in a compact design. Experimental results demonstrate the ability of SAREH to extract power from vehicles operating at speeds ranging from 180 to 1260 rpm. The maximum power output and corresponding power density are measured as 712 mW and 34 mW cm−3, respectively. The prototype successfully powers portable electronics and supports battery-free navigation, triaxial acceleration, and temperature multi-sensors during real road and railway simulation tests. Additionally, the SAREH operates as a highly sensitive speed sensor and an early-warning system for detecting the vehicle's motion state. These results represent a significant advancement in intelligent transportation systems by showcasing the practicality of self-powered wireless monitoring capabilities on vehicles. [Display omitted] • A novel speed-amplified energy harvesting technique is proposed. • The proposed system incorporates contra-rotating and friction pendulum mechanisms. • The prototype enables installation in confined spaces while achieving a high power density of up to 34 mW cm−3. • The prototype simultaneously powers daily electronics and supports battery-free wireless sensors. • The prototype operates as a speed sensor and an early-warning system for detecting the motion state of the vehicle. [ABSTRACT FROM AUTHOR]

[Display omitted] • The counterweight acts as the friction pendulum to form the all-in-one prototype. • The power density can reach 1982 W m−3. • The prototype can drive the daily used electric appliance and wireless sensor. • The prototype can serve as a speed sensor to detect the motion state of vehicles. • The prototype supports self-powered and real-time bogie frames monitoring. To maintain desirable service quality and operational safety, a wireless monitoring unit integrated with the vibration energy harvesting technology becomes an available choice to achieve self-powered, maintenance-free, and real-time monitoring of the train. However, owing to the bulky size and split design, to collect the mechanical energy from the bogie frame movement is still a considerable challenge for conventional harvesters. Here, we proposed a compact all-in-one on-rotor electromagnetic energy harvester. The key novelty is that a counterweight acts as the friction pendulum to produce the desired relative motion between the coils and magnet and make the device more easily install on the wheelset. Besides, the layout of the magnetic materials and coils is optimized to improve the conversion efficiency. The output performance under broad train speeds of 420–820 rpm is systematically studied to verify the improvements of the power density (up to 1982 W m−3), and the converted electricity successfully powers the daily electric appliance and the commercial wireless Bluetooth sensors. Additionally, the harvester serves as a speed sensor to detect the motion state of the vehicle. This work makes significant progress towards potential applications in the embedded self-powered wireless condition monitoring units. [ABSTRACT FROM AUTHOR]

• A multi-sensor information fusion covariance matrix scheme is presented for multi-sensor feature fusion. • A novel MCFD is proposed to demodulate fault modulation features from original vibration signals. • A MCFD-based framework is developed for multi-sensor driven gearbox intelligent fault diagnosis. • Experiment results demonstrated the effectiveness of the proposed MCFD-based framework. Accurate fault detection is extremely important to ensure stable gearbox operation. Data-driven schemes using cyclic spectral have received significant attention due to their robust demodulation performance. However, these schemes are mainly applied to process single sensor signals, and they are unable to accurately obtain precise fault features. This paper proposed a novel multiscale cyclic frequency demodulation (MCFD)-based feature fusion framework for multi-sensor driven gearbox intelligent fault diagnosis. Firstly, the MCFD is proposed to analyze the vibration signals from multi-sensor driven gearbox, which acquires the multi-sensor mode information without setting parameters in advance. Thereafter, the grey relational degree between the multi-sensor mode information and original signal is calculated, and its results are normalized to obtain the relationship coefficients. Finally, the acquired coefficients are performed for multi-sensor information fusion to form the covariance matrix for gearbox fault diagnosis. The effectiveness of the proposed feature fusion framework is validated using the gearbox case. The comparative experiments indicate that this framework outperforms comparative algorithms for multi-sensor driven gearbox fault diagnosis. [ABSTRACT FROM AUTHOR]

• ORS is developed to be installed on the rotating shaft for vibration measurements. • The developed ORS largely improves the SNR of vibration collections than OHS. • The FFT-based fault diagnosis theory for the rolling bearing is presented. • ORS can achieve easier and more robust Hilbert envelope analysis for diagnosis. • Results validate the performance of ORS for early rolling bearing fault diagnosis. The traditional on-house sensing (OHS) accelerometer for vibration measurements causes poor signal-to-noise ratio (SNR) and complicated fault modulations, which increases the difficulty and complexity for early bearing fault diagnosis. To overcome these challenges, this paper develops a wireless triaxial on-rotor sensing (ORS) system to largely improve the SNR and deduces fast Fourier transform (FFT) and Hilbert envelope analysis for accurate early rolling bearing fault diagnosis, which largely improves accuracy and efficiency for early fault diagnosis. First, the development of the ORS system for wireless vibration measurements is given. Second, the theoretical diagnostic relationships between dynamic ORS signals and rolling bearing faults are derived for FFT and Hilbert envelope analysis for the first time. Finally, the induction motor tests with outer and inner race faults successfully validate that both simple FFT and Hilbert envelope analysis can achieve more robust early rolling bearing fault diagnosis compared to OHS measurements. [ABSTRACT FROM AUTHOR]

• The proposed method extends the cyclic frequency range to Fs /2. • The designed scale factor in CWT can help locate important frequency bands. • TKEO is improved to process the single-carrier signal in the frequency domain. • The developed method can enhance the fault features effectively. • Induction motor tests validate the superiority of the method for early fault detection. The cyclostationary analysis techniques have been extensively explored for the purpose of fault detection in rotating machinery. However, there are still huge challenges because of both limited detection frequency range and low fault identification accuracy. This paper proposes an improved cyclostationary method to enhance incipient fault features. Firstly, the continuous wavelet transform is used to accurately locate important frequency bands, and the fault modulation mechanism or fast kurtogram can be adopted to design the optimal wavelet transform scale factor. Secondly, the Teager-Kaiser energy operator is improved to be used in the frequency domain for the weak fault feature enhancement. Finally, fault features are presented in the cyclic frequency domain through spectral coherence and enhanced envelope spectrum. The proposed method is verified through both numerical simulation and experiments, including incipient half-broken rotor bar, and rolling bearing outer race faults in induction motors. [ABSTRACT FROM AUTHOR]

• A multiscale denoising branch is developed to extract multi-level information and reduce noise interference. • An improved flow direction strategy-based adaptive resonance branch is introduced to learn fault-related periodic impulsive features. • Case studies are conducted to validate the efficacy of the developed IFD-MDCN. Rolling bearings are the core components of rotating machinery, and their normal operation is crucial to the entire industrial production. Most existing condition monitoring methods have been devoted to extracting discriminative features from vibration signals that reflect bearing health status information. However, the complex working conditions of rolling bearings often make the periodic impulsive characteristics related to fault information easily buried in noise interferences. Therefore, it is challenging for existing approaches to learning discriminative fault-related features in these scenarios. To address this issue, a novel multibranch CNN named IFD-MDCN is developed in this paper, which represents multibranch denoising convolutional networks (MDCN) with an improved flow direction (IFD) strategy. The main contributions of this work include: (1) designing a multiscale denoising branch to extract multi-level information and reduce noise impact. More specifically, the multiscale denoising branch adopts a Gaussian multi-level noise reduction procedure to represent vibration signals at multiple levels and filter out the noise components, and then it uses a multiscale convolutional module to extract abundant features from these denoised signal representations; (2) establishing an improved flow direction strategy-based adaptive resonance branch to learn periodic impulsive features associated with fault information from vibration signals. Extensive experimental results reveal that the IFD-MDCN outperforms five state-of-the-art approaches, especially in strong noise scenarios. [ABSTRACT FROM AUTHOR]

Highlights • A novel multi-stage noise reduction approach is developed. • EEMD and WT based denoising method is applied. • All IMFs are investigated and utilized for fault feature extraction. • MSB is used to extract fault feature for bearing fault diagnosis accurately. Abstract To extract impulsive feature from vibration signals with strong background noise and interference components for accurate bearing diagnostics. A multi-stage noise reduction method is proposed based on ensemble empirical mode decomposition (EEMD), wavelet thresholding (WT) and modulation signal bispectrum (MSB). Firstly, noisy vibration signals are applied with EEMD to obtain a list of intrinsic mode functions (IMFs) that leverage the desired modulation components to different degrees. Then, a number of initial IMFs in the high frequency range, which are separated by using the mean of the standardized accumulated modes (MSAM) to have more modulation contents, are further denoised by a wavelet thresholding approach. These cleaned IMFs in the high frequency are combined with the low frequency IMFs to construct a pre-denoised signal that maintains the modulation properties of the raw signal. Finally, modulation signal bispectrum (MSB) is used to extract the modulation signature by suppressing further the residual random noise and deterministic interference components. This multi-stage noise reduction method was validated through a simulation study and two experimental fault cases studies of rolling element bearing. The results were more accurate and reliable in diagnosing the bearing inner and outer race defects in comparison with the individual use of the state-of-the-art EEMD and MSB. [ABSTRACT FROM AUTHOR]

Clinch-bonded hybrid joining technology has been applied increasingly in different manufacturing disciplines. In this study, specimens of both similar and dissimilar sheets of H62 copper alloy, aluminium alloy and galvanised steel sheets were prepared in single-lap and T-joints. Tensile-shear tests and peeling tests were carried out for studying the load-bearing capacity and energy absorption of different clinch-bonded hybrid joints. The failure fractures were studied by a scanning electron microscope to characterise the different failure modes. Results show that the shear strength of the specimens, which results mainly from the adhesive, is better than their peeling strength, which is closely related to clinched structures. The mixed neck fracture exhibited the highest shear strength, and a better ability to absorb energy could be obtained by decreasing the material strength of the lower sheets in the tensile-shear test. In the peeling test, it was found that the best energy absorption ability could be achieved by the failure mode of upper sheet tearing, and improving the strength of the lower sheets resulted in greater joint peeling strength. [ABSTRACT FROM AUTHOR]

• A method to accurately calculate the blade characteristic frequency is proposed. • It relies on the physical relationship between the BPF and fundamental frequency. • Blade vibrations are separated using VKF with parameters optimization. • A framework for blade early diagnosis based on unsupervised learning is verified. Blade fault is a catastrophic failure of gas turbines. In order to improve the reliability of blade during operation, condition monitoring is one of the effective methods. However, there always exist two problems with blade monitoring: 1) challenges in warning of the occurrence of blade failure in advance and 2) difficulties finding the location after blade failure. In this article, we solve these problems by excavating the characteristics of blade-related signals in broadband casing vibration with advanced signal processing methods and machine learning technology. First, a novel method--Sparse Harmonic Product Spectrum (SHPS)--is proposed to accurately calculate blade passing frequency from gas turbine broadband casing vibration. The SPHS relies on Fourier transform, and its calculation utilizes the physical relationship between fundamental frequency and blade passing frequencies. Combining Vold-Kalman filter with adaptive parameter optimization process (AVKF), the blade-related vibration can be separated from casing vibration even in strong noise. Analysis of simulated casing vibration signal is used to verify the effectiveness and superiority of proposed method. Based on blade-related vibration, we build a gas turbine blade condition model in an unsupervised learning manner. The model can excavate potential blade failures earlier and more accurately than conventional threshold methods. Then, three coefficients are constructed according to the blade-related vibration characteristics to identify the blade fault location in multi-stage system. Moreover, the effectiveness of the proposed blade diagnosis framework is verified using actual industrial gas turbine blade fracturing failure data. [ABSTRACT FROM AUTHOR]

Fault diagnosis of rolling bearings has attracted extensive attention in industrial fields, which plays a vital role in guaranteeing the reliability, safety, and economical efficiency of mechanical systems. Traditional data-driven fault diagnosis methods require obtaining a dataset of full failure modes in advance as the training data. However, this kind of dataset is not always available in some critical industrial scenarios, which impairs the practicability of the data-driven fault diagnosis methods for various applications. A digital twin, which establishes a virtual representation of a physical entity to mirror its operating conditions, would make fault diagnosis of rolling bearings feasible when the fault data are insufficient. In this paper, we propose a novel digital twin-driven approach for implementing fault diagnosis of rolling bearings with insufficient training data. First, a dynamics-based virtual representation of rolling bearings is built to generate simulated data. Then, a Transformer-based network is developed to learn the knowledge of the simulated data for diagnostics. Meanwhile, a selective adversarial strategy is introduced to achieve cross-domain feature alignments in scenarios where the health conditions of the measured data are unknown. To this end, this study proposes a digital twin-driven fault diagnosis framework by using labeled simulated data and unlabeled measured data. The experimental results show that the proposed method can obtain high diagnostic performance when the real-world data is unlabeled and has unknown health conditions, proving that the proposed method has significant benefits for the health management of critical rolling bearings. • A digital twin-driven intelligent diagnosis method is developed. • A high-fidelity digital twin model is built for the rolling bearing. • A partial domain adaptation algorithm is introduced for bearing condition assessment. • One test is conducted to validate the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

This paper reports on studies of the fretting behavior of self-piercing riveted (SPR) joints in titanium sheet materials. Experiments were performed on SPR titanium joints to help understand the influences of sheet thickness and fatigue load level on the fatigue strength and failure mode of the joints. Failed joints were examined using a Scanning Electron Microscope and an Energy Dispersive X-ray machine to study the fretting failure mechanisms of the joints. The results showed that increasing sheet thickness could improve the fatigue strength of the joints at high load levels but less so at low load level. Three failure modes were observed, the pierced sheet failure mode, the locked sheet failure mode and the rivet failure mode. The pierced sheet failure was mainly caused by the fretting wear at the interface between the rivet head and the pierced sheet. The locked sheet failure and rivet failure were generated by the fretting wear at the interface between the rivet shank and the locked sheet. Oxidized wear debris was observed on the surfaces of all the fracture regions of the joints. The main elements of the oxidized wear debris on rivet surfaces were Ti, Zn and O. [ABSTRACT FROM AUTHOR]

Deep learning-based fault diagnosis methods have made tremendous progress in recent years; however, most of these methods are coarse grained and data demanding that cannot find the root causes of mechanical system failures at a finer granularity with limited fault data. Therefore, in this study, we first investigate the few-shot fine-grained fault diagnosis (FSFGFD) problem, with the aim of identifying novel fine-grained faults under different working conditions using only few samples from each class. To address the difficulties of fine-grained fault feature extraction and poor model generalization to unseen few-shot faults in FSFGFD tasks, a novel attention-based deep meta-transfer learning (ADMTL) method is proposed. First, the failure modes under different working conditions are considered as fine-grained faults, and their raw signals are transformed into time–frequency images. Based on this, an attention mechanism is introduced to guide the feature extractor of the ADMTL on what information to learn. The ADMTL then follows a three-stage learning process of pre-training, meta-transfer, and meta-adaptation to achieve fast adaptation to new fine-grained faults using a priori knowledge gained from known faults. Furthermore, a parameter modulation strategy is employed to adaptively update the pre-trained network during the meta-transfer process. The comprehensive experimental results of three case studies demonstrate the superiority of our method over state-of-the-art methods. The proposed method achieves excellent performance with an average accuracy of 99.08%, 95.86%, and 77.74% for FSFGFD tasks when performing meta-transfer within the same machine and between different machines, respectively. [ABSTRACT FROM AUTHOR]

• Improved cyclic spectral covariance matrix (ICSCM) is presented for multi-sensor data fusion. • An efficient demodulation algorithm ICS is proposed to discriminative features from different rotating machinery fault types. • ICSCM fully preserve the interaction relationship between different sensors. • The merits of ICSCM are demonstrated by using two sets of experimental case. When an abnormal situation occurs in rotating machinery, fault feature information may be scattered on multiple sensors, and fault feature extraction through a single sensor is not enough for fault detection. Moreover, fault detection techniques based on vibration signals are commonly applied to monitor the health of rotating machinery. However, the installation of vibration sensor is inconvenient, which will greatly affect collected signal and thus influence detection effect. This paper proposes a novel method with improved cyclic spectral covariance matrix (ICSCM) and motor current signal analysis, which achieves multi-sensor data fusion for rotating machinery fault detection. Firstly, an improved cyclic spectral is proposed to process multi-sensor signals collected from rotating machinery, which adaptively acquires multi-sensor mode components. Subsequently, sample entropy of acquired mode components is utilized to construct the ICSCM, which can fully preserve the interaction relationship between different sensors. Finally, ICSCM is incorporated into extreme learning machine classifier to identify different fault types for rotating machinery. The merits of the proposed method are validated using two datasets. Analysis results demonstrate that the proposed method has achieved satisfactory results and more reliable diagnosis accuracy than other state-of-the-art algorithms in rotating machinery fault detection. [ABSTRACT FROM AUTHOR]

• Time-varying dynamic coefficients of journal bearing are estimated without the tachometers. • Instantaneous angular speed of the journal bearing-rotor system is extracted. • An iteration strategy based on the square-root cubature Kalman filter is formulated. Accurate estimation of time-varying dynamic coefficients (TVDCs) of the journal bearing is important for the dynamic characteristic analysis of rotating machinery. This paper proposes a novel estimation method to identify TVDCs of journal bearings without the tachometers. Firstly, a phase-based method is introduced to extract the instantaneous angular speed (IAS) from the shaft displacement. Secondly, an iteration strategy based on the square-root cubature Kalman filter (SRCKF) is developed to estimate the TVDCs in the time domain. The state-space model and the measured shaft displacements of the journal bearing-rotor system have been applied in the estimation process. The proposed method can estimate TVDCs of journal bearing under speed-variable conditions without a tachometer by combining the phase-based and SRCKF methods. Finally, the simulation and experiments studies are conducted to demonstrate the effectiveness of the proposed methods. The results show that the proposed method can efficiently estimate TVDCs of the journal bearing under speed-variable conditions and at varied measurement noise levels. [ABSTRACT FROM AUTHOR]

[Display omitted] • Mesh stiffness model of opening-crack tooth with elastoplastic deflection is built. • The model can accurately estimate the mesh stiffness of gears with crack failure. • Impacts of failure severity on the mesh stiffness and load sharing are analyzed. • Tooth deflection affects mesh stiffness, load sharing and meshing zone ratio. In the gear transmission system, tooth root crack often occurs due to the impact of machining technology and cyclic load. The appearance of tooth root cracks will affect its mesh stiffness and change the vibration characteristics of the system, thereby reducing system reliability and service life. In previous studies on the modeling of the mesh stiffness of cracked gears, the cracks were often supposed to be in a closed state, and the elastoplastic deflection of the fault gear teeth was also neglected, resulting in significant errors in the estimation of the mesh stiffness. When a tooth root crack occurs, the engaged gear teeth will deviate from the theoretical meshing position due to the elastoplastic deflection of the fault tooth. Therefore, an accurate mesh stiffness estimation model in view of the opening crack and elastoplastic deflection of the cracked tooth is put forward in this study. Firstly, the actual meshing position of the tooth pairs is derived considering the elastoplastic deformation in the crack opening state; Then, the improved potential energy method is adopted to calculate the mesh stiffness, and the finite element method (FEM) is applied to verify it. At last, the influence of failure severity and elastoplastic deflection degree on mesh stiffness and load sharing is analyzed. The results show that the severity of the crack failure and elastoplastic deflection has a significant impact on the mesh stiffness and load-bearing. This research can provide stiffness input for the study of fault dynamics of the gear transmission system. [ABSTRACT FROM AUTHOR]

The fatigue performances of self-piercing riveting (SPR) joints connecting similar and dissimilar sheets of TA1 titanium alloy (TA1), Al5052 aluminium alloy (Al5052) and H62 copper alloy (H62) were studied in this paper. The specimens of similar TA1 sheets treated with stress relief annealing were prepared to investigate the influence of relief annealing on the mechanical properties of SPR joints. Fatigue tests were conducted to characterize the fatigue lives and failure modes of the joints. Scanning electron microscopy (SEM) was employed to determine the fatigue failure mechanisms of the joints. The results showed that stress relief annealing has an apparent impact on the fatigue performances but has little influence on the static strengths of the joints. For SPR joints in similar or dissimilar sheets, joints between similar TA1 sheets possess the best fatigue resistance, which are significantly higher than the joints in dissimilar sheets. In addition, most of the joints failed in the rivet and the locked sheet through fatigue tests, except the joints between TA1 and Al5052, which failed only in the locked sheets. [ABSTRACT FROM AUTHOR]

The mechanical properties of extensible die clinched joints in titanium sheet materials were investigated in this paper. Tensile–shear tests were carried out to characterize the mechanical properties of different clinched joints made of the similar titanium sheets and the dissimilar metal sheets combinations. The normal hypothesis tests were performed to examine the rationality of the test data. The load-bearing capacity, energy absorption and failure modes of different titanium sheets clinched joints were studied. Results showed that almost all titanium sheets clinched joints failed in the neck fracture mode. Results also showed that the load-bearing capacity and energy absorption of clinch joints with titanium as upper sheets are higher than that of the clinched joints with titanium as lower sheets. [ABSTRACT FROM AUTHOR]

This paper deals with the mechanical properties of self-piercing riveted joints in similar and dissimilar metal sheets of Al5052 aluminum alloy and H62 copper alloy. Force–displacement curves and S – N curves were obtained to characterize the properties of joints. The normal hypothesis tests were performed to examine the rationality of the test data. The strength, stiffness, impact resistance, failure modes and failure mechanism of joints were also studied. Results showed that the fatigue strength of the joints is influenced by the properties of the sheet materials being joined. The existence of fretting might accelerate the initiation and propagation of the cracks or change the direction of fatigue cracking. Both static strength and fatigue strength of the joints increase with the enhancement of the joint stiffness. The failure model and failure mechanism are affected by the stiffness of the joints. [ABSTRACT FROM AUTHOR]

With an increasing application of clinching in different industrial fields, the demand for a better understanding of the knowledge of static and dynamic characteristics of the clinched joints is required. In this paper, the clinching process and tensile–shear failure of the clinched joints have been numerically simulated using finite element (FE) method. For validating the numerical simulations, experimental tests on specimens made of aluminium alloy have been carried out. The results obtained from tests agreed fairly well with the computational simulation. Tensile–shear tests were carried out to measure the ultimate tensile–shear strengths of the clinching joints and clinching-bonded hybrid joints. Deformation and failure of joints under tensile–shear loading were studied. The normal hypothesis tests were performed to examine the rationality of the test data. This work was also aimed at evaluating experimentally and comparing the strength and energy absorption of the clinched joints and clinching-bonded hybrid joints. [ABSTRACT FROM AUTHOR]

• A NFE is proposed to quantify the fault information distribution in the SCoh. • A WCES from the SCoh with normalized weight is proposed as fault detector. • WCES can effectively extract fault information distributed in multiple bands. • WCES delivers superior bearing diagnostic performance than SES, EES and IES. Enhanced envelope spectrum (EES) and improved envelope spectrum (IES) generated from spectral coherence (SCoh) are proven to be more robust fault detection tools than squared envelope spectrum (SES). However, EES cannot effectively detect the fault-induced components under strong interference noise and IES can only capture the information of a fault-sensitive resonance spectral frequency band. To overcome these problems, weighted combined envelope spectrum (WCES) from SCoh is proposed as a novel fault detector. WCES integrates the fault components distributed in multiple resonance frequency bands using normalized feature energy and removes the envelope spectrum slices with less fault information to exclude disturbance noises. The performance of WCES is validated using simulations and experiments and compared with the advanced envelope spectra. The results demonstrate that WCES can effectively detect bearing faults under strong interference noise and multiple resonances compared with the SES, EES and IES, and has potential application value in bearing diagnostics. [ABSTRACT FROM AUTHOR]

In this paper, the acoustic based condition monitoring of a diesel engine is investigated. Firstly, an experimental test rig is set up to simulate engine faults. Acoustic signals are measured from the test diesel engine under different conditions. To analyse the measured acoustic signals, a two-dimensional topological seif-organising map (SOM) network is employed in this paper to perform feature extraction. The extracted features are processed by both statistical and spectral methods. The results show that the extracted features are able to show the differences between the engine''s normal and faulty conditions. [Copyright &y& Elsevier]

• A real-time novelty detection approach for industrial gas turbines. • Fusing multi-type monitoring parameters to establish a performance deviation model. • Using deviation curves to set the alarm threshold by extreme function theory. • Detecting abnormality accurately and sensitively based on only normal data. Novelty detection is crucial to ensure the availability and reliability of an industrial gas turbine. With the application of modern health monitoring systems, there is an ample amount of data gathered from gas turbines, however they are usually from normal events with limited knowledge of any novelty. In current practice, the unknown event is detected by comparing with a model of normality through pointwise approaches, which is inefficient in terms of false alarms or missing alarms. This paper proposes an accurate novelty detection approach using performance deviation model and extreme function theory. The model is established from the multi-sensor real-time performance data. Outputs of the model, that is, the deviation curves, are considered as functions instead of individual data points to test the status of the system as 'normal' or 'abnormal' by the extreme value theory. The effectiveness of the proposed approach is demonstrated by the monitoring data from a single shaft gas turbine on site. Compared with other traditional methods, the proposed approach is superior in terms of high detection accuracy and high sensitivity with a good balance between the false alarm rate and missing alarm rate. This paper provides a reliable approach for the real-time health monitoring of the industrial gas turbines. [ABSTRACT FROM AUTHOR]

• Non-Gaussian properties and misalignment are applied in tribofilm thickness. • Christensen's methods are extended by introducing the Gram-Charlier expansion. • Negative skewness and high kurtosis increase the misaligned journal bearing performances. • Non-Gaussian property impact on journal bearing is verified by simulations and experiments. In the hydrodynamic lubricated journal bearing system, the surface roughness and angular misalignment are two critical factors that affect bearing's performance. In this paper, the coupled effects of different non-Gaussian properties and misalignments are investigated on the performance of journal bearings. Christensen's stochastic model is extended by improving the probability density function of random roughness heights, which incorporates the Gram-Charlier expansion including skewness and kurtosis. In comparison with a Gaussian surface, the non-Gaussian rough surface has more significant influence on the bearing static performance. The negative skewness and large kurtosis increase the load capacity and decrease the friction coefficient. According to the simulations and experiments, non-Gaussian properties have more impact on the performance than misalignment when the journal bearing is operated in hydrodynamic lubrication regime based on different pressure distributions and vibration responses. These novel findings provide the basis for monitoring the conditions of hydrodynamic journal bearings. [ABSTRACT FROM AUTHOR]

• Fracture curve tilted along gear axis is modeled using analytic geometry method. • Mesh stiffness of gear with tooth tip chipping is analyzed by the proposed model. • The influences of fault severity on the vaiation of mesh stiffness are investigated. • The proposed model can evaluate mesh stiffness of gears accurately. Tooth tip chipping that the top of a tooth breaks away from lower portion is one of the common gear faults which will affect the dynamic characteristics of gears. In the assumption of plane-fracture tooth tip chipping, the tooth surface fracture line tilted configuration relatively to the gear axis is a curve, which is simplified to a straight in previous studies. However, such simplification will cause an estimate error of mesh stiffness. Therefore, to accurately estimate the mesh stiffness, failure models of tooth tip chipping for both internal gear and external gear are established by means of analytic geometry method in this paper. The fracture curve Eq. is derived by using analytic geometry method. And the mesh stiffness of the fault gear is derived via potential energy method and verified by the finite element method (FEM). The influence of different fault severity on the mesh stiffness is analyzed. The analysis results show that the proposed model can accurately evaluate the mesh stiffness of gears with tooth tip chipping and the modeling method can also be applied to other fault types. [ABSTRACT FROM AUTHOR]

This paper investigates the mechanism and characteristics of Acoustic Emission (AE) generated from the dynamic Fluid-Asperities Shearing (FAS) in the hydrodynamic lubrication (HL) regime. Firstly, a FAS model is derived to take into account the dynamic effect of surface asperities. Then, the influence of surface profiles, lubricants and operating conditions are illustrated on AE characteristics, i.e. magnitudes and frequency bandwidths. It has been found that the correlation length of surface roughness parameters and shear rate are two main factors affecting FAS behaviours and consequently AE characteristics. Finally, the corresponding experiments are carried out based on a rheometer rig, which validate the predictability of the model and new findings, paving the foundation for developing AE based monitoring techniques. • AE generated from the dynamic fluid-asperity shearing in the hydrodynamic lubrication is analysed. • A model of dynamic FAS regarding the spatial power spectral density of a non-Gaussian surface is derived. • The frequency bandwidth of AE signals depending on the shear rate and the correlation length is verified. • The simulations and the experimental measurements at the different conditions validate the predictability of the model. [ABSTRACT FROM AUTHOR]

• An enhanced MSB based non-Gaussian noise reduction method is proposed. • An AR model is developed as a pre-filter process unit to reduce the non-Gaussian noise. • The performance of fault feature extraction of the proposed AR-MSB is tested with various data types and bearing fault cases. • AR-MSB has high accuracy in fault feature extraction compared with the conventional MSB and FK. Many methods have been developed for machinery fault diagnosis addressing only Gaussian noise reduction, the major weaknesses of these methods are their performance for non-Gaussian noise suppression. Modulation signal bispectrum (MSB) is a useful signal processing method with significant advantages over traditional spectral analysis as it has the unique properties of Gaussian noise elimination and demodulation. However, it is susceptible to non-Gaussian noise that normally occurs in the practical applications. In view of the deficiency of MSB, in this paper, an autoregressive (AR) modeling filter was developed based on non-Gaussian noise suppression to improve the performance of MSB for machinery fault detection. The AR model was considered as a pre-filter process unit to reduce the non-Gaussian noise. And the order of the AR model, which can affect the performance of the AR filter, was determined adaptively using a kurtosis-based indicator. However, the existing nonlinear modulation components remain in the AR filtered signal. The MSB was then applied to decompose the modulated components and eliminate the Gaussian noise from the filtered signal using AR model for the fault feature extraction accurately. The advantage of the AR model can effectively manage non-Gaussian noise, whereas the MSB can suppress Gaussian noise and is illustrated in the simulation signal. Furthermore, the proposed AR-MSB method was applied to analyze the vibration signals of defective bearings with outer race and inner race faults. By comparison, the proposed approach had a superior performance over conventional MSB and fast kurtogram in extracting fault features and was precise and effective for rolling element bearing fault diagnosis. [ABSTRACT FROM AUTHOR]

Fretting wear has a considerable effect on the fatigue properties of clinched joints and is the most important cause of fatigue failure in them. Foam metal is a new type of structural material that can be sandwiched into a clinched joint to improve its mechanical properties with respect to fretting wear. In the work reported here, clinched single-lap and cross-lap joints with and without foam copper layers were prepared and subjected to static load testing. Fatigue tests were conducted on the single-lap joints to analyse the fatigue properties and failure modes of the aluminium-alloy clinched and foam metal sandwich clinched joints. The damage caused in single-lap joints by fretting wear was assessed by examining joint cross-sections before completely fatigue broken. Scanning electron microscopy and energy spectrometry were used to examine the failure fractures of the two kinds of joints and to investigate the fretting wear failure mechanism. The incorporation of the foam copper layer increased the shear strength of the clinched joint by 5% but had little effect on the peeling strength. It also increased the energy absorption capacity of the joint under a shear force by 33%, but reduced it under a peeling load by 27%. The fretting wear of the clinched joint occurred mainly at the contact surface of the two sheets in the lap area and at the clinched neck. Fretting wear generated oxide wear debris and caused micro-cracks on the sheet surface and these micro-cracks then continuously expanded until sheet fracture failure occurred. The foam copper layer effectively reduced the fretting wear between the upper and lower sheets and greatly enhanced the fatigue life of the aluminium-alloy clinched joint. [ABSTRACT FROM AUTHOR]