Bearing Fault Feature Extraction Research Articles

Research on the Sparse Optimization Method of Periodic Weights and its Application in Bearing Fault Diagnosis

The vibration state monitoring signal of rolling bearings usually shows periodic transient impulse characteristics at the time of the fault, but in the fault sprouting stage and actual monitoring, its impulse components are easily disturbed by random pulses, harmonics, background noise, etc., which increases the difficulty of extracting the periodic shock characteristics of the fault. This paper proposed a rolling bearing fault feature extraction method based on sparse coefficient weighting theory and periodic enhancement strategy to effectively extract weak periodic shock features with a low signal-to-noise ratio. In the proposed period weighted sparse wavelet representation (PWSWR) method, firstly, based on the sparse wavelet model, the mean kurtosis (MK) index was introduced as a weight to distinguish the contribution of the wavelet coefficients of each signal component. Secondly, a sparse signal period enhancement strategy was proposed using the improved envelope harmonic product spectrum period estimation method. The strategy is embedded into the sparse representation model to achieve the periodic enhancement of filtered signals. Finally, the envelope detection of the sparse signal was carried out to identify the fault. The results of bearing fault simulation signals and two engineering test signals showed that the proposed method could effectively extract weak fault features of bearings and had certain advantages compared with other sparse methods.

Research on Fault Feature Extraction Method of Rolling Bearing Based on SSA–VMD–MCKD

In response to the problem that nonlinear and non-stationary rolling bearing fault signals are easily disturbed by noise, which leads to the difficulty of fault feature extraction, to take full advantage of the superiority of variational mode decomposition (VMD) in noise reduction, and of maximum correlation kurtosis deconvolution (MCKD) in highlighting continuous pulses masked by noise, a method based on sparrow search algorithm (SSA), VMD, and MCKD is proposed, namely, SSA–VM–MCKD, for rolling bearing faint fault extraction. To improve the feature extraction effect, the method uses the inverse of the peak factor squared of the envelope spectrum as the fitness function, and the parameters to be determined in both algorithms are searched adaptively by SSA. Firstly, the parameter-optimized VMD is used to decompose the fault signal to obtain the intrinsic mode function (IMF) components, from which the optimal mode component is selected, and then the optimal component signal is deconvoluted by the parameter-optimized MCKD to enhance the periodic fault pulses in the optimal component signal, and finally extracts the rolling bearing fault characteristic frequency by envelope demodulation. Experiments on simulated signals and measured data show that the method can adaptively determine the parameters in VMD and MCKD, enhance the fault impact components in the signals, and effectively extract the fault characteristic frequencies of rolling bearings, with a success rate up to 100%, providing a new idea for rolling bearing fault feature extraction.

A Novel Method for Enhanced Demodulation of Bearing Fault Signals Based on Acoustic Metamaterials

Rolling bearings fault diagnosis technology plays a significant role in modern industries. In this article, a novel method to diagnose bearing faults via acoustic metamaterials is proposed and experimentally validated by selecting and enhancing the optimal resonance frequency band for demodulation. The proposed method is performed by analysing acoustic signals, which is convenient due to its noncontact measurement. Different from conventional denoising techniques, the proposed method is implemented by enhancing the resonance frequency band via acoustic metamaterials instead of constructing filters to wipe out noise. Consequently, all the useful information of fault signals are remained and the detection limits of current acoustic sensing systems are improved because of the enhanced fault signals. Additionally, the optimal resonance band is sought by changing measured positions based on the frequency-selective enhancement property of the designed metamaterial. By comparing with spectral kurtosis-based method, the proposed method is more superior to extract bearing fault features at low signal to noise ratios. All the presented results indicate that the proposed method is effective to extract bearing fault features.

Research on mathematical morphological operators for fault diagnosis of rolling element bearings

Morphological filtering adopting the combined morphological operators (CMOs) has been widely used to extract bearing fault features from vibration signals. However, few studies focus on the comprehensive performance of various CMOs under different interferences. In this paper, several new CMOs for feature extraction are proposed firstly, and then the impulse extraction performance of fourteen typical CMOs in the presence of harmonic interference, random impulses and background noise is investigated through simulations. To enhance the capability of impulse extraction and noise elimination, the morphological hat cross-correlation operator (MHCCO) is constructed through the cross-correlation of two CMOs with excellent performance. Additionally, an improved strategy is proposed to adaptively determine the optimal length of the structural element for MHCCO. Simulations, experiments and comparisons demonstrate the effectiveness and superiority of the proposed method. This paper provides important guidance for selecting CMO for feature extraction and an effective method for bearing fault diagnosis.

Sparsity enforced time–frequency decomposition in the Bayesian framework for bearing fault feature extraction under time-varying conditions

Fault characteristic extraction of rolling bearings is essential for fault diagnosis. Rolling bearings are usually operated at changing speeds, and the nonstationary signals of the bearings are covered by the heavy background noise, making the extraction task of fault features very difficult. To address this issue, a robust fault characteristic extraction approach based on the time–frequency analysis under variable speed conditions is proposed in this paper. Firstly, the sparse property of the time-variant fault characteristics and low-rankness of background noise are explored and utilized in the time–frequency representation (TFR). Then, the sparse and the low-rank components are integrated into a hierarchical Bayesian model, and a random error term is considered to make the Bayesian model more robust. The Gibbs sampler is applied to extract the desired sparsity-enhanced component of the TFR in the Bayesian framework. Eventually, the time–frequency reassignment technique is adopted further to optimize the time–frequency resolution of the sparse component. Two simulated scenarios and a real-data experiment are used to evaluate the suggested approach’s performance. It turns out that the proposed approach is robust to noise and can extract the bearing time-varying fault features effectively.

An adaptive spectrum segmentation-based optimized VMD method and its application in rolling bearing fault diagnosis

Variational mode decomposition (VMD) is a signal decomposition algorithm with excellent denoising ability. However, the drawback that VMD is unable to determine the input parameters adaptively seriously affects the decomposition results. For this issue, an optimized VMD method based on modified scale-space representation (MSSR-VMD) is proposed. Firstly, MSSR is proposed to segment the fault signal spectrum, acquiring modes’ number and the initial center frequency for each mode adaptively. Moreover, a pre-decomposition step is added to the original VMD, which selects a target mode from divided frequency bands. Finally, the penalty factor of the target mode is adjusted during the iterative update of the VMD to achieve accurate extraction for the fault features. MSSR-VMD and other adaptive decomposition algorithms are employed to handle the simulated and experimental signals separately. By comparing the analysis results, the method has certain superiority in rolling bearing fault feature extraction.

Weak Fault Feature Extraction Method Based on Improved Stochastic Resonance.

Aiming at the problems of early weak fault feature extraction of bearings in rotating machinery, an improved stochastic resonance (SR) is proposed combined with the advantage of SR to enhance weak characteristic signals with noise energy. Firstly, according to the characteristics of the large parameters of the actual fault signal, the amplitude transform coefficient and frequency transform coefficient are introduced to convert the large parameter signal into small parameter signal which can be processed by SR, and the relationship of second-order parameters are introduced. Secondly, a comprehensive evaluation index (CEI) consisted of power spectrum kurtosis, correlation coefficient, structural similarity, root mean square error, and approximate entropy, is constructed through BP neural network. Moreover, this CEI is adopted as fitness function to search the optimal damping coefficient and amplitude transform coefficient with adaptive weight particle swarm optimization (PSO). Finally, according to the improved optimal SR system, the weak fault feature can be extracted. The simulation and experiment verify the effectiveness of the proposed method compared with traditional second-order general scale transform adaptive SR.

Fault Diagnosis of Rotating Equipment Bearing Based on EEMD and Improved Sparse Representation Algorithm

Aiming at the problem that the vibration signals of rolling bearings working in a harsh environment are mixed with many harmonic components and noise signals, while the traditional sparse representation algorithm takes a long time to calculate and has a limited accuracy, a bearing fault feature extraction method based on the ensemble empirical mode decomposition (EEMD) algorithm and improved sparse representation is proposed. Firstly, an improved orthogonal matching pursuit (adapOMP) algorithm is used to separate the harmonic components in the signal to obtain the filtered signal. The processed signal is decomposed by EEMD, and the signal with a kurtosis greater than three is reconstructed. Then, Hankel matrix transformation is carried out to construct the learning dictionary. The K-singular value decomposition (K-SVD) algorithm using the improved termination criterion makes the algorithm have a certain adaptability, and the reconstructed signal is constructed by processing the EEMD results. Through the comparative analysis of the three methods under strong noise, although the K-SVD algorithm can produce good results after being processed by the adapOMP algorithm, the effect of the algorithm is not obvious in the low-frequency range. The method proposed in this paper can effectively extract the impact component from the signal. This will have a positive effect on the extraction of rotating machinery impact features in complex noise environments.

Proportional Selection Scheme: A Frequency Band Division Tool for Rolling Element Bearing Diagnostics

The cyclic spectral coherence analysis has been proved to be a useful tool to reveal the hidden periodic or repetitive patterns, and how to determine an optimal demodulation band is crucial for characterizing the fault characteristic of the rolling element bearing (REB). Thus, a proportional selection scheme based on a bottom-up strategy is proposed in this study, which can determine an optimal demodulation band and realize bearing fault feature extraction under the conditions of poor frequency resolution and a lower signal-to-noise ratio (SNR). First, compared with conventional frequency division structures, such as the 1/3-binary tree structure, the finer frequency band groups with the same bandwidth in each division level can be obtained automatically and strictly under poor frequency resolution conditions. Second, the richness of the REB fault information of each divided frequency band can be evaluated by the improved diagnostic feature indicator under a lower SNR condition. By using the scheme, the fault characteristic of the REB can be exposed. The efficacy of the proposed scheme is supported by simulations and experiments under some tough conditions

Bearing Fault Feature Extraction Method Based on Enhanced Differential Product Weighted Morphological Filtering.

Aimed at the problem of fault characteristic information bearing vibration signals being easily submerged in some background noise and harmonic interference, a new algorithm named enhanced differential product weighted morphological filtering (EDPWMF) is proposed for bearing fault feature extraction. In this method, an enhanced differential product weighted morphological operator (EDPWO) is first constructed by means of infusing the differential product operation and weighted operation into four basic combination morphological operators. Subsequently, aiming at the disadvantage of the parameter selection of the structuring element (SE) of EDPWO depending on artificial experience, an index named fault feature ratio (FFR) is employed to automatically determine the flat SE length of EDPWO and search for the optimal weighting correlation factors. The fault diagnosis results of simulation signals and experimental bearing fault signals show that the proposed method can effectively extract bearing fault feature information from raw bearing vibration signals containing noise interference. Moreover, the filtering result obtained by the proposed method is better than that of traditional morphological filtering methods (e.g., AVG, STH and EMDF) through comparative analysis. This study provides a reference value for the construction of advanced morphological analysis methods.

Hierarchical dispersion entropy and its application in fault diagnosis of rolling bearing

Aiming at the disadvantage that multi-scale entropy only analyzes the low-frequency components of time series, this paper proposes a rolling bearing fault feature extraction method based on hierarchical dispersion entropy (HDE). Firstly, the HDE is constructed based on the hierarchical operators and dispersion entropy. Secondly, the influence of HDE parameters on entropy stability is studied. Finally, the fault diagnosis method based on HDE, least square support vector machine is applied in rolling bearings. The experiment results show that the proposed method can distinguish different types of the rolling bearing faults. It owns the higher fault diagnosis accuracy than the traditional multi scale entropy methods, such as multi-scale sample entropy (MSE), multi-scale dispersion entropy (MDE).

Selective weighted multi-scale morphological filter for fault feature extraction of rolling bearings

Morphological filtering shows effectiveness in vibration signal analysis because of its simplicity and efficiency. Considering that different structural elements have different effects on filtering results, a new multi-scale morphological filtering (MMF) method called selective weighted multi-scale morphological filter (SWMMF) is developed for integrating results of different scales based on adaptive weighting strategy. Firstly, four morphological operators (dilation–closing, closing–dilation, erosion–opening and opening–erosion) are integrated into a new combination difference morphological filter to strengthen effect of faulty component extraction. Secondly, this new morphological filter is further extended to multiple scales in order to overcome limitation of single scale filter. Finally, the filtered results of different scales are adaptively combined by using the whale optimization algorithm (WOA)-based selective weighting method. The effectiveness of multi-scale filter and selective weights is proved by comparing with single-scale and average weighting filter on simulation and real-world cases (bearing vibration signals with different defects). The testing results on vibration signals indicate that SWMMF is able to extract effectively defect frequency and the corresponding multiplication frequencies from bearing vibration signals with heavy noise. The testing results illustrate that SWMMF outperforms other representative MMFs (e.g., weighted multi-scale morphological gradient operator (WMMG), weighted multi-scale difference operator (WMDIF), weighted multi-scale average operator (WMAVG)) on impulsive feature extraction of bearing vibrations signals with various defects. Moreover, it is demonstrated that SWMMF has good applicability in bearing fault diagnosis due to setup of adaptive weights and selection of structure element.

Weak Fault Feature Extraction of Rolling Bearings Based on Adaptive Variational Modal Decomposition and Multiscale Fuzzy Entropy.

The working environment of rotating machines is complex, and their key components are prone to failure. The early fault diagnosis of rolling bearings is of great significance; however, extracting the single scale fault feature of the early weak fault of rolling bearings is not enough to fully characterize the fault feature information of a weak signal. Therefore, aiming at the problem that the early fault feature information of rolling bearings in a complex environment is weak and the important parameters of Variational Modal Decomposition (VMD) depend on engineering experience, a fault feature extraction method based on the combination of Adaptive Variational Modal Decomposition (AVMD) and optimized Multiscale Fuzzy Entropy (MFE) is proposed in this study. Firstly, the correlation coefficient is used to calculate the correlation between the modal components decomposed by VMD and the original signal, and the threshold of the correlation coefficient is set to optimize the selection of the modal number . Secondly, taking Skewness (Ske) as the objective function, the parameters of MFE embedding dimension M, scale factor S and time delay T are optimized by the Particle Swarm Optimization (PSO) algorithm. Using optimized MFE to calculate the modal components obtained by AVMD, the MFE feature vector of each frequency band is obtained, and the MFE feature set is constructed. Finally, the simulation signals are used to verify the effectiveness of the Adaptive Variational Modal Decomposition, and the Drivetrain Dynamics Simulator (DDS) are used to complete the comparison test between the proposed method and the traditional method. The experimental results show that this method can effectively extract the fault features of rolling bearings in multiple frequency bands, characterize more weak fault information, and has higher fault diagnosis accuracy.

Cyclic correlation density decomposition based on a sparse and low-rank model for weak fault feature extraction of rolling bearings

The weak fault feature extraction of rolling element bearings is of critical interest for fault diagnosis. The initial fault is always very weak and buried in high background noise, making it extremely hard to extract the fault feature. Thus, it is essential to correctly extract the weak fault featureof the rolling bearings. A weak fault feature extraction approach using Cyclic correlation density decomposition based on the sparse and low-rank model is proposed in this article. According to the cyclic statistical properties of the fault-bearing signal, the Fast Spectral Correlation (Fast-SC) algorithm is employed to obtain the Cyclic Spectral Density (CSD). It is founded that the CSD of periodic impulse exhibits a high degree of sparsity. Then, the sparsity is exploited into the sparse and low-rank decomposition model to extract the fault features. The CSD is decomposed into two components with the Robust Principal Component Analysis (RPCA) algorithm. The sparse component corresponds to the periodic fault impulse, while the low-rank component represents interference. It is noteworthy that the decomposed sparse component exhibits high resolution and high sparsity, which means the bearing fault features can be revealed clearly and accurately. Finally, the rolling bearing fault feature is detected effectively by the Enhanced Envelope Spectrum (EES). Both simulation fault signal and experimental data are analyzed to verify the proposed method's performance. A Frequency Component Indicator (FCI) and a Relative Indicator Gain (RIG) are constructed to quantify the comparison of the extraction results. The RIG of the proposed method relative to the envelope spectrum is more than 10 dB in the simulated bearing signal with a low SNR of-10 dB, and more than 6 dB in the experimental case. These results validate that the proposed method can extract weak fault features more effectively than some existing methods.

Rolling bearing fault feature extraction via improved SSD and a singular-value energy autocorrelation coefficient spectrum

It is usually difficult to extract weak fault features from rolling bearing vibration signals under noise pollution. To address this problem, a fault feature extraction approach for rolling bearings using improved singular spectrum decomposition (SSD) and a singular-value energy autocorrelation coefficient spectrum (SVEACS) is proposed. Firstly, to facilitate the determination of the optimal modal parameters in the SSD algorithm, the number of SSD layers is adaptively determined using an improved SSD algorithm based on permutation entropy. Then, the optimal modal components are selected, and the proposed SVEACS is used to determine the order of singular-value noise reduction. Finally, envelope analysis is used to extract the accurate shock characteristics of the denoised signal. The results of the experiments on simulated and real signals indicate that the proposed method can effectively extract the weak characteristics of the vibration signal under strong noise, and accurately diagnose the fault of a rolling bearing.

First-order differential filtering spectrum division method and information fusion multi-scale network for fault diagnosis of bearings under different loads

In recent years, data-driven intelligent diagnosis methods have been widely applied in the field of bearing fault diagnosis. However, these methods involve some expert experience and knowledge, and cannot accurately mine bearing fault characteristics under different loads. To solve this problem, this paper proposes a first-order differential filtering spectrum division (FDFSD) method and an information fusion multi-scale network (IFMSNet) to realize bearing fault diagnosis under different working conditions. First of all, the proposed spectrum division method based on the first-order differential filtering, the first-order differential processing of time domain signals, and the introduction of triangular filter, reclassify the spectrum features, highlight feature information, can accurately extract bearing fault features. Secondly, a new multi-scale network model of information fusion is constructed in this paper. Convolution kernels of different sizes are used to extract fault features of bearings of different scales, and information fusion is carried out to identify bearing working conditions and realize intelligent diagnosis of bearings under different loads. Finally, in order to verify the effectiveness and accuracy of the proposed method, it is verified on a variety of bearing experimental data sets. The results show that the average prediction accuracy of the proposed method is 99.11% and 97.74%, respectively. Compared with the proposed three single-scale network, K-nearest neighbor, Naive Bayes, support vector machine and random forest methods, the proposed method has more advantages in bearing intelligent diagnosis under different loads.

An iterative morphological difference product wavelet for weak fault feature extraction in rolling bearing fault diagnosis

Weak fault feature extraction is of great significance to the fault diagnosis of rolling bearing. At the early stage of defects, fault features are usually weak and easily submerged in strong background noise, which makes feature information extremely difficult to be excavated. This paper proposes an iterative morphological difference product wavelet (MDPW) to address this issue. In this scheme, firstly, the morphological difference product filter (MDPF) is developed using the combination morphological filter-hat transform operator and difference operator. The MDPF is then incorporated into a morphological undecimated wavelet to construct the MDPW, which can achieve noise suppression and fault feature enhancement. Subsequently, the optimal iteration numbers that influence the performance of MDPW is determined using the fault severity indicator, which effectively extracts periodic impulse related to the failure of rolling bearing. Finally, the fault identification is inferred by the occurrence of fault defect frequencies in the MDPW spectrum with the optimal iteration numbers. The validity of the iterative MDPW is evaluated through numerical simulations and experiment cases. The analysis results demonstrate that the iterative MDPW has higher diagnosis accuracy than existing algorithms (e.g., adaptive single-scale morphological wavelet and weighted multi-scale morphological wavelet). This research provides a new perspective for improving the weak fault feature extraction of rolling bearing.

Research on an ID-PCA Early Fault Detection Method for Rolling Bearings

Since the rolling bearing is complex during the signal acquisition process, there is a certain loss during the process of collecting the vibration signal. This has led to the weakness of the early fault characteristics of the rolling bearing, affecting the accuracy of the rolling bearing fault feature extraction. In response to the above problems, an early fault detection method based on the Improved Deep Principal Component Analysis (ID-PCA) is proposed. The proposed method uses the time-series characteristic information of the vibration signal to establish a model, which solves the problem that the principal component analysis method cannot detect the vibration signal directly. Through the deep decomposition theorem, a multi-layer data processing model is established to fully mine the weak fault features in the vibration signal. It can solve the problem of inaccurate early fault detection results due to weak fault feature information. The reliability of this method is proved theoretically through sensitivity analysis. Finally, through experimental simulation, the accuracy and feasibility of this method are proved from the perspective of practice.

Research on Fault Feature Extraction and Pattern Recognition of Rolling Bearing

Rolling bearings play a very important role. If the state of the rolling bearing is wrong, the whole equipment will fail, so we need to check the state of the running bearing. Fault Feature Extraction of Rolling Bearings. CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) analyzes the collected oscillation signals to obtain various natural state functions. Kurtosis and Correlation Factor Evaluation Index are used to screen IMF components with a large number of error data, and independent component analysis is carried out on the selected components. The corresponding processing and reconstruction are carried out to extract the feature frequency. The built-in signal processing method can deal with the problem of bearing fault signal and identify the specific fault frequency. Fault Pattern Recognition of Rolling Bearings. The decomposition acceleration sensor is used to analyze the data of bearing in different states, and then, several components are obtained. The components related to the error signal are found, and the corresponding feature vectors are calculated. Then, the dimension vector is reduced, and a particle flow optimization algorithm is used to identify specific error conditions after dimension reduction. Experiments show that this method has higher detection rate in identifying specific fault states.

Sparse enhancement based on the total variational denoising for fault feature extraction of rolling element bearings

As one of the essential parts of the mechanical transmission system, rolling bearing is vital to ensure the safe operation of mechanical equipment. The rolling bearing goes through four stages from its installation to the end of its life: normal operation, early weak failure, serious failure, and failure. If faults can be found in the early failure stage of the whole life cycle and maintenance strategies can be adopted in time, the safe and trouble-free operation of the equipment can be guaranteed. However, the fault features are not apparent in the early failure stage of the bearing’s full life cycle. Moreover, being completely submerged in strong background noise can easily occur, making early fault diagnosis challenging. This study presents a new sparse enhancement model based on kurtosis-wavelet total variation denoising (Kurt-WATV) for early fault feature extraction. Firstly, a sparse optimization model is constructed to extract the early fault feature, and the original signal is decomposed by the over-complete rational discrete wavelet transform (ORDWT). Then a fast iterative algorithm is deduced to solve the established sparse optimization model, and the optimal wavelet subband is selected by Kurt-WATV, which is reconstructed to the fault signal. Finally, the bearing test data from bearing’s full-life cycle are adopted to illustrate the effectiveness and robustness of the proposed method. Results confirm that the established method can achieve excellent performance in early fault feature extraction.