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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]

• 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]

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]

• 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]

Rolling bearings are important parts of mechanical equipment. However, the early failures of the bearing are usually masked by heavy noise. This brings about difficulties to the extraction of its fault features. Therefore, there is a need to develop a reliable method for early fault detection of the bearing. Considering this issue, a novel fault diagnosis method using the improved wavelet threshold denoising and fast spectral correlation (Fast-SC) is proposed. First, to solve the discontinuity of the hard threshold function and avoid the constant deviation triggered by the soft threshold function, a piecewise continuous threshold function is proposed by using a new threshold selection rule to denoise the original signal. In the new threshold function, the adjuster α is introduced to improve the traditional wavelet denoising algorithm, so as to enhance the signal-to-noise ratio (SNR) of the original signal more effectively. Then, the denoised signal is analysed by Fast-SC to identify the rolling bearing fault features. Finally, simulation analysis and experimental data demonstrate that the proposed approach is effective for rolling bearing fault detection compared with Fast-SC and the combined method based on traditional wavelet threshold and Fast-SC. [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]

Rolling element bearings are one of the critical elements in rotating machinery of energy engineering systems. A defective roller of bearing moves in and out of the load zone during each revolution of the cage. Larger amplitude impact transients (LAITs) are produced when the defective roller passes the load zone centre and the defective area strikes the inner or outer races. A series of LAIT segments with higher signal to noise ratio are separated from a continuous vibration signal according to the bearing geometry and kinematics. In order to eliminate the phase errors between different LAIT segments that can arise from rotational speed fluctuations and roller slippages, unbiased autocorrelation is introduced to align the phases of LAIT segments. The unbiased autocorrelation signals make the ensemble averaging more accurate, and hence, archive enhanced diagnostic signatures, which are denoted as LAIT-AEAs for brevity. The diagnostic method based on LAIT separation and autocorrelation ensemble average (AEA) is evaluated with the datasets captured from real bearings of two different experiment benches. The validation results of the LAIT-AEAs are compared with the squared envelope spectrums (SESs) yielded based on two state-of-the-art techniques of Fast Kurtogram and Autogram. [ABSTRACT FROM AUTHOR]

In this paper, a novel model for fault detection of rolling bearing is proposed. It is based on a high-performance support vector machine (SVM) that is developed with a multifeature fusion and self-regulating particle swarm optimization (SRPSO). The fundamental of multikernel least square support vector machine (MK-LS-SVM) is overviewed to identify a classifier that allows multidimension features from empirical mode decomposition (EMD) to be fused with high generalization property. Then the multidimension parameters of the MK-LS-SVM are configured by the SRPSO for further performance improvement. Finally, the proposed model is evaluated through experiments and comparative studies. The results prove its effectiveness in detecting and classifying bearing faults. [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]

[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]

• 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]

• 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]

• The nonstationarity of vibration signals from rolling bearings can be tuned by envelope to be more stationary. • EAAE denoise is based the ensemble average of phase aligned autocorrelation signals. • EAAE is able to reveal the weak bearing fault signatures under SNR of-30 dB. The rolling element bearings are extensively applied in rotating machines, and they are the most susceptible components in rotating machines. Early fault detection of bearings is to prevent machines from such typical failures and subsequent consequences. In this paper a detector based on Ensemble Average of Autocorrelated Envelopes (EAAE) is proposed to identify the early occurrence faults in rolling element bearings, of which the fault induced vibration signals are inevitably contaminated or masked by both additive background noise and random phase noise (or slippage between bearing components). To enhance the cyclostationary characteristics for fault detection, it utilizes the phase synchronization property of autocorrelation signals for aligning the cyclostationary signals in the lag domain to achieve an effective ensemble average which allows both types of random influences to be suppressed significantly. As a result, this detector shows very high performance of robustness in extracting the local fault signatures, which is verified by simulation signals and experimental investigations and benchmarked by the recent milestone method of Spectral Correlation (SC). [ABSTRACT FROM AUTHOR]

As the critical parts of wind turbines, rolling bearings are prone to faults due to the extreme operating conditions. To avoid the influence of the faults on wind turbine performance and asset damages, many methods have been developed to monitor the health of bearings by accurately analyzing their vibration signals. Stochastic resonance (SR)-based signal enhancement is one of effective methods to extract the characteristic frequencies of weak fault signals. This paper constructs a new SR model, which is established based on the joint properties of both Power Function Type Single-Well and Woods-Saxon (PWS), and used to make fault frequency easy to detect. However, the collected vibration signals usually contain strong noise interference, which leads to poor effect when using the SR analysis method alone. Therefore, this paper combines the Fourier Decomposition Method (FDM) and SR to improve the detection accuracy of bearing fault signals feature. Here, the FDM is an alternative method of empirical mode decomposition (EMD), which is widely used in nonlinear signal analysis to eliminate the interference of low-frequency coupled signals. In this paper, a new stochastic resonance model (PWS) is constructed and combined with FDM to enhance the vibration signals of the input and output shaft of the wind turbine gearbox bearing, make the bearing fault signals can be easily detected. The results show that the combination of the two methods can detect the frequency of a bearing failure, thereby reminding maintenance personnel to urgently develop a maintenance plan. [ABSTRACT FROM AUTHOR]