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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]

Failures of bearing would cause sudden downtime and severe accidents that may require significant maintenance and economic consequences. Therefore, condition monitoring of bearing is extremely important for ensuring reliable and efficient operation. The analysis of electric power supply signals offers a non-intrusive and cost-effective method for detecting various incipient faults in rotary machines. However, using inverter or variable speed drive would influence the electric power supply signals, causing difficulties in detection and diagnosis results. To reduce these influences and develop effective detection techniques, this paper focuses on using electric power supply signals as an approach to detect incipient faults in the bearing of driving induction motor based on high-order bispectral analysis. It takes the advantage of providing abundant information from electric signatures of downstream driving motors to improve the performance of bearing's health monitoring. Modulation signal bispectrum (MSB) and cyclic bispectrum (CCB) exhibit significance to detect normal and abnormal bearing conditions, whereas remarkable effort is dedicated from MSB result due to the inclusion of phase information with amplitude modulation in both lower and higher sidebands. It can conclude that effective measurements of electrical power supply signals utilize reliable fault diagnosis information in the mechanical components of the driving motor. [ABSTRACT FROM AUTHOR]

34441193 8 Xiao Y., Xue J., Li M., Yang W. Low-pass filtering empirical wavelet transform machine learning based fault diagnosis for combined fault of wind turbines. However, the majority of the wind turbine fault diagnosis research is focused on single faults rather than combined faults. They took into account the discrepancy of the fault features shown by different fault types, and formed the feature center for fault diagnosis by extracting the features of the samples of each fault type. [Extracted from the article]

IAN873, Dongfang93114 and Reyan73397, created through vegetative propagation for their high yield and excellent cold resistance, are major clones planted in China. In this work, latexes with rubber particles of the same size from these clones are separated from fresh natural rubber latex, and corresponding rubber films are prepared from each latex. The structure and components of each film are measured. This indicates that the characteristics of the rubbers obtained from latexes with similar particle sizes show some resembling trends among different clones, while for specific samples, those characteristics vary depending on the clone. The molecular weight is generally highest in IAN873 and lowest in Reyan73397. Rubber chains in small rubber particles are longer, and large rubber particles show a wider molecular weight distribution. The gel content of every sample from Reyan73397 is lower than the other two clones. The nitrogen content increases with the size of rubber particles in all clones. The ester content of small rubber particles in IAN873 and Reyan73397 is almost zero. Large rubber particles have more branching points formed via esters. This study provides a new perspective on the influence of clones on the relationship between characteristics of natural rubber and the size of rubber particles in natural rubber latex. [ABSTRACT FROM AUTHOR]

Maintaining exceptional product quality and boosting processing efficiency requires precise evaluation of various aspects of the turning process, including the cutting depth, feed rate, and size of the workpiece. This article presents a novel approach for observing the turning process state using modulation signal bispectrum (MSB) and motor current signals. A nonlinear model was established that clarifies the load torque oscillations during turning, which in turn affects the amplitude and phase modulation of the motor stator current. Random noise can be efficiently minimized using the MSB algorithm, allowing the extraction of the current-modulation characteristic sideband phase and amplitude from the collected current signal. This technique enables clear representation and enhanced monitoring of load torque changes throughout the turning process. The proposed method was validated via mathematical simulations and universal lathe tests, with the results indicating that the MSB phase and amplitude values effectively capture both dynamic and static torque alterations during the turning operation, making this approach a valuable tool for overseeing the turning process. [ABSTRACT FROM AUTHOR]

Global orbits connect the saddle points in an infinite period through the homoclinic and heteroclinic types of manifolds. Different from the periodic movement analysis, it requires special strategies to obtain expression of the orbit and detect the associated profound dynamic behaviors, such as chaos. In this paper, a global dynamic frequency method is applied to detect the homoclinic and heteroclinic bifurcation of the complicated nonlinear systems. The so-called dynamic frequency refers to the newly introduced frequency that varies with time t, unlike the usual static variable. This new method obtains the critical bifurcation value as well as the analytic expression of the orbit by using a standard five-step hyperbolic function-balancing procedure, which represents the influence of the higher harmonic terms on the global orbit and leads to a significant reduction of calculation workload. Moreover, a new homoclinic manifold analysis maps the periodic excitation onto the target global manifold that transfers the chaos discussion of non-autonomous systems into the orbit computation of the general autonomous system. That strategy unifies the global bifurcation analysis into a standard orbit approximation procedure. The numerical simulation results are shown to compare with the predictions. [ABSTRACT FROM AUTHOR]

The novelty of this paper is to numerically investigate the effect of manufacturing error and misalignment on the transient lubrication behavior of dynamically loaded journal bearings with micro-groove. Based on the average Reynolds equation considering the mass conservation cavitation algorithm, the asperity contact model, and the force balance equation, the mixed lubrication model under time-varying dynamic loads is developed. Meanwhile, mathematical functions are given for the bearings with different surface profiles, including the circumferential and axial manufacturing errors of the bearing, the horizontal and vertical deflection angles of the journal, and the micro-grooves with different distribution forms. According to this model, the lubrication characteristics of the bearing are systematically investigated at different horizontal and vertical deflection angles, amplitudes, spatial numbers, and phase angles of the circumferential waviness, as well as shape profiles of the axial error. In addition, the performance parameters of dynamically loaded journal bearings with ideal and actual surfaces under different micro-groove distributions are comparatively evaluated. Numerical results show that manufacturing errors and misalignment have a remarkable effect on the transient behavior of dynamically loaded journal bearings, and the coupling effect will be more consistent with practical engineering. It can be found that the distribution form of the micro-groove surface directly affects the friction reduction effect of bearing systems. The numerical model can serve as a meaningful guideline for the optimum design of dynamically loaded journal bearing with micro-groove. [ABSTRACT FROM AUTHOR]

• A full generalization of Gini index is proposed for sparse quantification. • Fully generalized Gini indices are theoretically proven to be sparsity measures. • Two key performances of fully generalized Gini indices are investigated. • Fully generalized Gini indices exhibit excellent performance in characterizing repetitive transients. • Fully generalized Gini indices can effectively achieve bearing condition monitoring and fault diagnosis. The classic Gini index (GI) is generalized recently by using nonlinear weight sequences as sparsity measures for sparse quantification and machine condition monitoring. The generalized GIs with different weight parameters are more robust to random transients. However, they show insufficient performance in discriminating repetitive transients under noise contamination. To overcome this shortage, this paper proposes a two-parameter generalization method to tune not only the weight parameter but also the norm order, allowing for a full generalization of the classic GI to quantify transient features and leading to new statistical indicators which are named fully generalized GIs (FGGIs). Mathematical derivations show that FGGIs satisfy at least four of the six typical attributes of sparsity measures and that those with weight parameter equal to one satisfy at least five sparse attributes, proving that they are a new family of sparsity measures. Numerical simulations demonstrate that FGGIs can monotonically evaluate the sparseness of the signals and that the FGGIs with appropriate parameters exhibit improved performance in resisting random transient interferences and discriminating noise-contaminated repetitive transients compared to traditional sparsity measures. The performance of FGGIs in the condition monitoring of rolling element bearings is validated using two different run-to-failure experiment datasets, including a gradual failure and a sudden failure. The results show that increasing the norm order can improve the capability of FGGIs to characterize transient fault features, allowing more accurate trending of bearing health conditions, and therefore achieving better condition monitoring performance than the traditional sparsity measures. [ABSTRACT FROM AUTHOR]

This paper numerically investigates the effect of turbulence and journal misalignment on the lubrication characteristics of micro-grooved bearings with low-viscosity lubricant. The generalized average Reynolds equation satisfying the mass conservation cavitation algorithm is developed by integrating the average flow model proposed by Patir and Cheng, the Ng–Pan turbulent model, and the P- Θ model proposed by Elrod and Adams. With this model, the finite difference method is used in the numerical procedure. Moreover, the mathematical models of micro-grooves with different bottom shapes, that is, rectangle, isosceles triangle, left triangle, and right triangle, are given. The validity of the proposed model is verified by the comparisons with the published literature. Based on numerical simulation, the minimum film thickness, eccentricity ratio, attitude angle, maximum film pressure, friction torque, misalignment moment, film thickness, and pressure distributions under different external loads, rotational speeds, radial clearances, misalignment angles, and micro-groove parameters between models with and without turbulence and misalignment are comparatively analyzed. The numerical results reveal that turbulence may occur under heavy external load, high rotational speed, and large radius clearance. Concurrently, turbulence increases the minimum fluid film thickness and attitude angle, decreases the eccentricity ratio and friction torque, and enhances the bearing capacity. Furthermore, the larger misalignment angle results in the smaller minimum film thickness, eccentricity ratio and attitude angle, and the larger maximum film pressure, misalignment moment, and axial tilt of film pressure. Numerical simulations can provide theoretical guidance for the optimization of the geometrical parameters of micro-grooved bearings. [ABSTRACT FROM AUTHOR]

Grinding residual stress (GRS) has been understood to have a great impact on the strength of gear fatigue life. However, there are lack of detailed studies on the influences in terms of mechanism and magnitude. To fill this gap, this study proposed a new method based on finite element analysis (FEA) to calculate the overall meshing stress on a gear tooth surface by including both the GRS and elastohydrodynamic lubrication (EHL) film pressure. Applying this method to a spiral bevel gear has resulted in a number of interesting results that GRS produced to reveal its mechanism of action through a full meshing cycle. The acquired understanding would be useful for development of manufacturing processes. [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]

• Residual unbalance and bearing dynamic coefficients are estimated simultaneously. • Dual augmented Kalman filter is applied successfully in the estimation method. • The method performs a strong robustness in different measurement noise levels. • The reliability of the method is verified in different measurement configurations. Simultaneous estimation of the residual unbalance and bearing dynamic coefficients is of great significance to the dynamic analysis and rotor design of rotating machinery. However, traditional estimation methods are susceptible to routine scattering caused by the ill-posed problem and require external excitation forces, making them inconvenient and costly for engineering applications. Therefore, a novel simultaneous estimation method based on the augmented state-space model of the system combined with the dual augmented Kalman filter (DAKF) method is proposed for the double-disc rotor-bearing system without the external excitation force. The principle of this method is designed through an iterative strategy in the time domain, which only requires the steady-state unbalance responses of the two selected locations. To prevent the coupling relationship between the unbalance and the bearing dynamic coefficients from affecting the results, the estimation of the bearing dynamic coefficients is indirectly converted to the determination of the equivalent oil-film force according to the dynamic theory. Besides, features of the coupling relationship are investigated by the sensitivity analyses, proving that the coupling relationship has a significant influence on the estimation performance, especially for the bearing dynamic coefficients. Furthermore, simulations and experimental studies show that the proposed method can effectively estimate the bearing dynamic coefficients and unbalance parameters under different measurement noise levels, rotation speeds and measurement configurations. This study provides a comprehensive investigation into the estimation of dynamic characteristics and thereby establishes a reliable foundation for further assessment of the rotor-bearing system's overall performance. [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]

• A metastructure-aided bearing fault signal enhancing diagnosis method is proposed. • Drum-shaped metastructure creates selective frequency band enhancement. • Artificially adjustable resonance for full band demodulation. • Cost-efficient rolling bearing fault diagnosis with low sampling rate. Rolling bearings, extensively utilized in rotating machinery, have critical significance for online fault diagnosis in the domains of industrial equipment maintenance and accident prevention. Presently, fault diagnosis methods heavily rely on identifying the optimal resonance band in the high-frequency range (several kHz) to achieve high signal-to-noise ratio (SNR) fault information. However, these approaches, which necessitate high sampling rate sensing systems and complex algorithm deployments, contradict the practical requirements for cost-effective sensors and edge computing in online diagnosis. To address this contradiction, this paper introduces a novel Drum-shaped Metastructure (DMS) to enhance weak bearing fault signals, thus promoting the detection performance of conventional sensors. The DMS is constructed with a drumhead metastructure consisting of a central block mass attached with four spiral beams, of which its length can be adjusted by a tunable frequency mechanism (TFM) for different frequency responses of interest. The detail of its selective frequency enhancement characteristics is first studied through numerical simulations within a frequency range of 200 Hz to 1000 Hz. Subsequently, the effectiveness of the weak signal enhancement is verified on various bearing test systems, which utilize a prototype DMS fabricated by 3D printing. The results present a significant enhancement in the SNRs of the bearing fault signal, which is achieved by demodulating the full frequency band without the need for complex algorithms. Therefore, the proposed DMS provides a new cost-efficient approach to weak bearing fault diagnosis and online monitoring. [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]

This paper presents an investigation into sideband vibro-acoustics suppression of the permanent magnet synchronous motor (PMSM), associated with several periodic signal-based dithering modulations. Mathematical derivations of four typical periodic signal-based schemes are deduced with the power spectral density (PSD) method. The individual modulation parameters, such as bandwidth and modulation ratio, are determined the values to minimize the sideband vibro-acoustics. The frequency distributions of the sideband components are discussed by the analytical derivations. The relationship between the electromagnetic components and mechanical responses is reconstructed by coupling with temporal features. For verification, the experimental tests with a prototype PMSM are introduced under different operational conditions. According to the results and figures, both magnitude suppression and frequency distribution are accurately correlated with sideband harmonic components as well as vibro-acoustic responses. This investigation thus clearly holds promise for the evaluation and optimization in powertrain used by electric vehicles. [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]

• A new calculation model of the tooth torsional deformation caused by the NDL along TWD is presented. • A double-layer iterative model for TVMS and load distribution of gears is proposed considering the effects of NDL along TWD. • The proposed model can achieve the decoupling calculation of TVMS and the load along TWD caused by the early-stage crack. • FE method proves the accuracy and high efficiency of the proposed model. • The effects of crack parameters and torque on the tooth torsional deformation, TVMS and load distribution are revealed. To reveal the coupling relationship between the time-varying mesh stiffness (TVMS) and the load distribution along the tooth width direction (TWD) of gears with early-stage crack (ESC), a double-layer iterative analytical model for the TVMS and load distributions of gears is proposed considering the effects of the non-uniformly distributed load (NDL) along TWD caused by the ESC. In the proposed model, an analytical model of tooth torsional deformation and a parallel slice stiffness model of the tooth pair with ESC are separately developed based on the slicing method. On this basis, a double-layer iterative calculation method for the TVMS and load distributions is proposed, in which the coupling relationships between the slice stiffness and load distribution along TWD as well as the TVMS and load distribution between the meshing tooth pairs are respectively presented with the inner- and outer- layer iterations. Finite element (FE) models are established to verify the proposed double-layer iterative model. The effects of crack parameters and applied torque on the tooth torsional deformation, TVMS, and load distributions of the gear with ESC are finally investigated based on the proposed model. The results show that the proposed model can realize the accurate and fast decoupling calculation of the TVMS and load distributions of gears with ESC. This study can provide a basis for the establishment of the refined ESC fault diagnosis method and the rapid evaluation of the load distributions of gears with asymmetric errors or faults along TWD. [ABSTRACT FROM AUTHOR]

[Display omitted] • Application of digital twin in life prediction of mechanical equipment. • Digital twin provide rich and independent virtual simulation data. • The improved CycleGAN can map simulation data in virtual space to measured data in physical space. • Large sample data can significantly improve the accuracy of various life prediction models. The prediction accuracy of the remaining useful life of rolling bearings is greatly affected by the size of sample data, and it is difficult to obtain enough fault samples in practical applications. Digital twin technology can reproduce the operation process of rolling bearings and other mechanical equipment in the digital world, which provides a new paradigm for life prediction under the condition of small samples. In this paper, a virtual and real combination of life-cycle rolling bearing digital twin is proposed. The modified CycleGAN combined with Wasserstein distance is used to map the simulation data in virtual space to the measured data in physical space, which significantly reduces the error between the simulation data and the measured data. The effectiveness of the improved rolling bearing digital twin and the availability of simulation data are verified by experiments. The simulation data are applied to the advanced remaining useful life prediction method, and the high-precision life prediction of rolling bearings is realized. The comparison with the traditional life prediction method verifies that the proposed method can effectively solve the small sample problem. [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]

• Generalized envelopes and generalized envelope spectra are proposed for signal demodulation. • The performance of generalized envelope spectra in rolling bearing fault diagnosis is revealed using simulations and experiments. • The product envelope spectrum is proposed as an enhanced spectral analysis tool. • Product Envelope Spectrum Optimization-gram (PESOgram) is developed for rolling bearing fault diagnosis. • PESOgram shows excellent capability in identifying bearing fault characteristic frequencies. The vibration signal of a faulty rolling bearing exhibits typical non-stationarity – often in the form of cyclostationarity. The spectrum tools often used to characterize cyclostationarity mainly include envelope spectrum, squared envelope spectrum and log-envelope spectrum. In this paper, new detection methods of cyclostationarity are developed for obtaining a larger family of envelope analysis and their effectiveness in rolling bearing fault diagnosis is evaluated rigorously. Firstly, based on the simplified Box-Cox transformation, the generalized envelope signals are constructed from the analytic signal for demodulation purposes, and then a spectrum family named generalized envelope spectra (GESs) is proposed to reveal cyclostationarity. Especially, GESs with different transformation parameters exhibit different performance advantages against the random impulse noise and Gaussian background noise which are commonly present in rolling bearing vibration signals. Subsequently, a novel spectrum tool that combines the performance advantages of different GESs, called product envelope spectrum (PES), is developed to strengthen the capability to detect cyclostationarity. Finally, an enhanced envelope analysis named Product Envelope Spectral Optimization-gram (PESOgram) is proposed to improve the accuracy and robustness of PES for rolling bearing fault diagnosis in the presence of different fault-unrelated interference noises. The performance of the PESOgram method is validated on numerically generated signal and experimental signals collected from two railway axle bearing test rigs and compared with several state-of-the-art envelope analysis methods. The results demonstrate the effectiveness of the proposed method for fault diagnosis of rolling bearings and its advantages over other state-of-the-art methods. [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]

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] • A compact energy harvester with the hybrid nonlinear mechanism is proposed. • The prototype can be installed on the limited space of freight trains. • The prototype can work at a wide bandwidth of 1–11 Hz under 0.5 g. • The prototype possesses excellent output stability in freight train operation. • The prototype supports self-powered and real-time freight train monitoring. To improve operational safety and system reliability, real-time wireless health monitoring systems are necessary for freight trains. However, due to the lack of onboard power and space restrictions, monitoring sensors rely on batteries as power sources, which are not eco-friendly and involve high maintenance costs for battery replacement. Therefore, developing self-powered maintenance-free wireless monitoring sensors integrated with energy harvesting is in urgent demand. Here, we propose a compact ultralow-frequency and broadband piezoelectric energy harvester (UBPEH) that can be easily installed in the limited space of the axle box to effectively harvest bogie lateral vibrations. The T-shaped UBPEH employs magnetic interaction to soften the stiffness and strengthen the stopper operation in a low-frequency range. To predict and optimize the prototype, we establish the model of UBPEH by considering the displacement, inclination angle, and shape of the magnets. Theoretical and experimental results show that the prototyped UBPEH might operate in the range of 1–11 Hz, covering the representative frequencies of bogie vibrations on freight trains. An output power of 605 μW on a matched resistance of 200 kΩ under the acceleration of 11 Hz and 0.5 g (g = 9.8 m s−2) is achieved, and the harvested electric power can successfully drive typical commercial wireless Bluetooth sensors. Furthermore, the harvester possesses output stability and mechanical durability under actual service hours of freight trains. The results of this work pave the way to implement self-powered wireless condition monitoring on freight trains. [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]

• An improved form of SWNSE is developed by generalizing the order of the norm. • A design framework for improved Gini indices is established and four specific forms of improved Gini index are proposed. • Improved Gini indices are proven to be sparsity measures. • A series of improved Gini indices are proposed for characterizing repetitive transients. • The improved Gini indices with norm order 3 exhibit better comprehensive performance. Sparsity measures are important and effective tools for accurately characterizing fault features and degradation trends of rotating machinery. In the past few decades, numerous sparsity measures, such as kurtosis, negentropy, Lp/Lq norm, pq-mean, smoothness index and Gini index, have been proposed and introduced for condition monitoring, fault diagnosis and remaining life prediction of rotating machinery. However, it is difficult for traditional sparsity measures to possess strong random impulse resistance and fault impulse discernibility simultaneously. To design robust sparsity measures for repetitive transient characterization and machine condition monitoring, an improved form of the sum of weighted normalized square envelope (SWNSE) is firstly developed by generalizing the order of the norm as a framework for adaptively quantifying the repetitive transients. Under this framework, a rank-dependent generalized Gini index (RDGGI) is proposed, allowing four specific forms of RDGGI to be reasonably constructed by introducing four weight sequence design methods. Theoretical derivation shows that the quantitative indices produced by the four specific forms of RDGGI satisfy at least five of the six attributes of sparsity measures, proving that the newly designed quantitative indices are sparsity measures, and are also scale-invariant and have a limited magnitude range between 0 and 1. Furthermore, a series of improved Gini indices are derived by setting typical norm orders and designing appropriate monotonically decreasing weight sequences, and simulation analysis is conducted to verify their three key properties in characterizing transient features. Finally, the performance of the improved Gini indices in characterizing bearing fault features for condition monitoring is verified using vibration datasets from two bearing run-to-failure experiments. Simulation and experimental results show that the improved Gini indices with norm order 3 can simultaneously possess strong resistance to random impulses and discernibility of fault impulses, demonstrating the superior performance of improved Gini indices in bearing condition monitoring. [ABSTRACT FROM AUTHOR]

• Torsional vibrations of the engine crankshaft-gearbox coupled system are investigated. • Instantaneous Angular Speed at the flywheel and the driven shaft are compared. • Natural frequencies under normal and cracked conditions are analysed in time domain. • IAS at the driven shaft contains information from both the engine and the external load. • The driven shaft is the better position for online monitoring sensor installation. Torsional vibration responses of an engine crankshaft-gearbox coupled system with misfire and breathing slant crack are investigated in this paper. The crankshaft is modelled by lumped mass method and its dynamic equations are deduced based on Lagrange equation. Both the time varying mesh stiffness of the gearbox composing of a pinion and a gear and the response-dependent stiffness of a shaft element with a breathing slant crack are evaluated. Natural frequencies of the healthy and cracked systems are analysed respectively. Torsional vibrations based on the instantaneous angular speed (IAS) of the flywheel and the driven shaft are compared among the operating conditions of normal, misfire and breathing slant crack on the driven shaft. Simulation results are validated by experiments, which show that the encoder on the driven shaft is better because it can monitor the IAS of the flywheel and the driven shaft simultaneously and the signals are more prominent here. [ABSTRACT FROM AUTHOR]

• Array signal processing is highly appreciated for camera-based vibration measurements. • Correlated pixels can be used to increase output SNR via constructive interference. • A sinusoid-based piecewise function is proposed to approximate mode shape. • An adaptive filtering algorithm is developed to match mode shape and enhance signal power. As a non-contact and full-field testing method, high-speed camera-based modal analysis has become a feasible and acknowledged approach. However, extracting small displacements from noisy images has experienced high level of difficulty, especially in high frequency range. This paper proposes a novel adaptive spatial filtering (beamforming) algorithm to extract the displacement signals using high-speed camera. In the proposed algorithm, one pixel is considered as a sensor measuring displacement and a set of pixels are therefore taken as the elements of sensor array. Then, an adaptive spatial filtering acting on this sensor array is proposed. The proposed approach mainly includes three steps. Firstly, a set of pixels are selected to compose a sensor array according to signal to distortion and noise (SINAD). Secondly, a node/antinode searching scheme is proposed based on sinusoid-based piecewise functions, which works as an adaptive filter to match mode shape and enhance modal displacement. Finally, the output of spatial filtering is adopted as the system response for the identification of modal parameters. To validate the performance of the proposed method, simulation and experiment studies are conducted based on measuring the vibration model properties of a free-free beam, which includes a comparison with LK optical flow method and conventional accelerometer-based method. The results show that the SNR of estimated displacement and computational efficiency is significantly improved without using additional sensors. The proposed method paves a way for broadening the applications of using high-speed camera for full-field vibration measurements. [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]

• A gear-shaft-bearing-housing dynamic model is proposed to reveal the modulation between bearing and gear. • Vibration Characteristics of bearing clearances under the effect of gear meshing is investigated. • An indicator is proposed based on MSB-SE for bearing clearances monitoring. • A run-to-failure gearbox test rig is designed to verify the effectiveness of the proposed indicator. Internal radial clearance is a key factor influencing bearing fatigue life. Moreover, bearings inevitably suffer from various wears and tears, which result in gradual increase of clearance and shorten bearing life. Monitoring bearing clearance changes using vibration can effectively indicate the bearing wear and provide good leading time to perform maintenances. Previous studies show that vibration at ball pass frequency on outer race (BPFO) can be based for clearance monitoring. However, such clearance induced vibration has not been well understood, especially under complicated dynamic interactions such as in a gearbox system. To fill this gap, this paper presents a nonlinear gear-shaft-bearing-housing vibration model with fourteen degree of freedom (DOF) to investigate the vibration responses under the dynamic gear meshing force and progressively changed radial clearances at first. Then, the model was verified through a two-stage spur gearbox. Furthermore, bearing characteristics with different radial clearances under the influence of gear are revealed and indicator based on modulation signal bispectrum-sideband estimator (MSB-SE) was proposed. Finally, vibration data from a run-to-failure gearbox test rig was utilized to verify the effectiveness of the MSB-SE indicator for bearing clearances monitoring. Simulation results show that BPFO is modulated on gear meshing frequency (GMF) and BPFO amplitude from envelope spectrum increases with bearing clearances under the influence of gear meshing. Indicator based on MSB-SE, possessing the capability of purifying the interferences of gear meshing and strong noises, is effective to capture the variance of bearing clearances. The experiment based on a run-to-failure gearbox test rig provided evidence for the effectiveness of the proposed indicator, which is more accurate than BPFO amplitude from conventional envelope analysis and time-domain indicators, such as RMS and kurtosis. These findings are of significance for bearing fault diagnosis and maintenance. [ABSTRACT FROM AUTHOR]