Rolling Bearing Fault Feature Research Articles

Rolling Bearing Fault Feature Extraction Method based on SSA-VMD and MOMEDA

To address the challenge of extracting bearing fault features, this study proposes a new rolling bearing fault feature extraction method based on the Sparrow Search Algorithm (SSA) to optimize Variational Mode Decomposition (VMD) and Multipoint Optimal Minimum Entropy Deconvolution with Convolution Adjustment (MOMEDA). Firstly, SSA is employed to identify optimal parameters in VMD, followed by the utilization of correlation coefficients and kurtosis to filter relevant Intrinsic Mode Function (IMF) components. Subsequently, MOMEDA is applied to denoise the reconstructed signal, mitigating the interference caused by pulse fault signals. Finally, the envelope spectrum analysis is conducted on the denoised signal. Experimental results demonstrate the efficacy of the proposed method in extracting fault features and mitigating noise interference.

Research on the Application of Variational Mode Decomposition Optimized by Snake Optimization Algorithm in Rolling Bearing Fault Diagnosis

The rolling bearing is one of the commonly used mechanical components in rotating machinery, and its health directly affects the normal operation of equipment. However, the fault signal of rolling bearing is susceptible to noise interference, which makes it difficult to extract the fault characteristics of the rolling bearing and thus affects the accuracy of the diagnosis results. To address this problem, this paper proposes a method by using a snake optimization algorithm to optimize variational mode decomposition (SOA-VMD) and applies it to the extraction of the fault feature of rolling bearing. First, the minimum Shannon entropy to kurtosis ratio (EKR) is used as the fitness function of SOA to search for the best parameter combination of VMD. Second, the optimized VMD is used to decompose the vibration signal of rolling bearing to obtain K intrinsic mode functions (IMFs). Then, the IMF with the most fault information is selected for reconstruction through EKR. The Teager–Kaiser energy operator (TKEO) spectrum analysis is performed on the reconstructed signal. Finally, this method is used to analyze the simulation signal and rolling bearing vibration signal and compared with empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) algorithms to verify the feasibility and effectiveness of the SOA-VMD method.

Early Fault Diagnosis of Bearings Based on Symplectic Geometry Mode Decomposition Guided by Optimal Weight Spectrum Index

When the rotating machinery fails, the signal generated by the faulty component often no longer maintains the original symmetry, which makes the vibration signal with nonlinear and non-stationary characteristics, and is easily affected by background noise and other equipment excitation sources. In the early stage of fault occurrence, the fault signal is weak and difficult to extract. Traditional fault diagnosis methods are not able to easily diagnose fault information. To address this issue, this paper proposes an early fault diagnosis method for symplectic geometry mode decomposition (SGMD) based on the optimal weight spectrum index (OWSI). Firstly, using normal and fault signals, the optimal weight spectrum is derived through convex optimization. Secondly, SGMD is used to decompose the fault signal, obtaining a series of symplectic geometric modal components (SGCs) and calculating the optimal weight index of each component signal. Finally, using the principle of maximizing the OWSI, sensitive components reflecting fault characteristics are selected, and the signal is reconstructed based on this index. Then, envelope analysis is performed on the sensitive components to extract early fault characteristics of rolling bearings. OWSI can effectively distinguish the interference components in fault signals using normal signals, while SGMD has the characteristic of unchanged phase space structure, which can effectively ensure the integrity of internal features in data. Using actual fault data of rolling bearings for verification, the results show that the proposed method can effectively extract sensitive components that reflect fault characteristics. Compared with existing methods such as Variational Mode Decomposition (VMD), Feature Mode Decomposition (FMD), and Spectral Kurtosis (SK), this method has better performance.

Fault feature signal extraction method for rolling bearings in gas turbine engines based on threshold parameter decision screening

Vibrations during the operation of gas turbine engines (GTEs) include shocks from the combustion process as well as vibrations from high-speed rotating components, which may mask or distort the characteristic signals of rolling bearing failure. Compared to those of ordinary rolling bearings, the fault characteristics of rolling bearings in GTEs are more difficult to extract. In response to the difficulty of extracting characteristic signals of rolling bearing faults in gas turbine engines due to the effect of environmental noise, a method for extracting characteristic features of GTE rolling bearings is proposed. First, the particle swarm optimization (PSO) algorithm is used to optimize the parameters in the variational mode decomposition (VMD) algorithm, resulting in K0 modal components. VMD can not only suppress the modal aliasing phenomenon, but also decompose signals at different scales, thereby providing multi-resolution information. Second, a new parameter harmonization equation is introduced, which balances the relationship between the kurtosis and correlation coefficients, and is fused into a single parameter P. Subsequently, high signal-to-noise ratio (SNR) signals are selected ted based on threshold parameter criteria, and these high-SNR signals are used to generate new vibration signals. Finally, the envelope spectrum is used to extract fault characteristics of bearings. According to the results, the fault feature signal extraction (FFSE) method can extract fault characteristics in rolling bearings under both simple and complex transmission paths.

Multiscale permutation entropy based on natural visibility graph and its application to rolling bearing fault diagnosis

Rolling bearings being important components of mechanical equipment, the accurate fault diagnosis method of rolling bearings is of great importance to ensure production safety. Permutation entropy is a nonlinear measure of the irregularity of time series, which involves calculating permutation patterns, that is, defining permutations by comparing adjacent values of the time series. When using graph signal processing technology to analyze the vibration signal of rolling bearing, the natural visibility graph (NVG) can better reflect the dynamic characteristics of the vibration signal than path graph (PG). In this paper, the multiscale permutation entropy (MPE) is defined on NVG, and it is used to characterize the different fault characteristics of rolling bearings. The sand cat swarm optimization (SCSO) algorithm is employed to optimize the parameters of support vector machine (SVM); The MPEs of different faults of rolling bearing which defined on NVG are regarded as the fault feature set input into optimized SVM, and it is applied to characterize the different fault characteristics of rolling bearings, realizing fault diagnosis of rolling bearing. The proposed method is used to analyze the experimental data which contain both normal and faulty rolling bearings. The experiment results show that the proposed method can diagnose the bearing faults effectively. The MPE based on NVG is superior to MPE based on PG and MPE based on the vibration signal in distinguishing the different damage states of rolling bearings. The classification accuracy of optimized SVM based on SCSO algorithm is higher than other classical models. The effectiveness and feasibility of defining entropy on the graph signal and as the fault feature vectors for rolling bearing to realize fault diagnosis is validated. The results indicate that the proposed method can effectively detect bearing faults, and demonstrate its effectiveness and robustness for rolling bearing fault diagnosis.

Rolling bearing fault diagnosis based on variational mode decomposition and weighted multidimensional feature entropy fusion

Since bearing fault signal in complex running status is usually characterized as nonlinear and non-stationary, it is difficult to extract accurate affluent features and achieve effective fault identification via conventional signal processing tools. In this article, a rolling bearing fault diagnosis technique based on variational mode decomposition and weighted multidimensional feature entropy fusion is proposed to address this issue, which is mainly composed of three procedures. First, the original signal undergoes the variational model decomposition. Next, the signal features are extracted by weighted multidimensional feature entropy as the input of the diagnosis model. Finally, the classification is performed by a convolutional neural network. The method is applied in simulation and experimental analysis. The experimental results show that the proposed method, which demonstrates strong immunity to noise and robustness, can more effectively and adaptively extract the fault features of rolling bearings and achieve the goal of identifying the rolling bearing fault category and damage degree under variable operating conditions. Meanwhile, this approach exhibits superior accuracy and identification performance to some similar entropy-based hybrid approaches referred to in this article, with a promising prospect in industrial application.

Fault diagnosis of rolling bearing based on the three-dimensional coupled periodic potential-induced stochastic resonance

Abstract Conventional bistable and monostable stochastic resonance (SR) methods exhibit certain limitations in their capacity to enhance and extract incipient characteristics. Firstly, the inherent potential function structure, characterized by a singular stable-state paradigm, proves inadequate in accommodating the heterogeneous and multifaceted condition monitoring signals. Secondly, the interconnected dynamic characteristics of the mechanical signals remain unaccounted for. Furthermore, conventional SR methods persist in utilizing a fixed constant as the critical system parameter, thereby neglecting the synergistic interaction among monitoring signals, potential function structures, and scale factors. Owing to the rich dynamic characteristics of the three-dimensional multi-stable coupled periodic potential SR system, it demonstrates superior noise utilization compared to monostable and bistable systems. In view of this, the present formulates a three-dimensional spatial model employing a coupled periodic potential model with nonlinear coupling. Subsequently, a pioneering method for diagnosing rolling bearing faults is introduced, utilizing the framework of three-dimensional multi-stable coupled periodic potential-induced SR. Simulation and experimental results illustrate that this approach effectively enhances and extracts the subtle fault characteristics of rolling bearings, ensuring a clear distinction between the spectral peak at the bearing fault characteristic frequency and the spectral peak originating from the interference noise.

A new approach to adaptive VMD based on SSA for rolling bearing fault feature extraction

Due to the structure of rolling bearings, will have various problems. So the early detection of rolling bearing faults is very important. Consequently, a precise method for extracting fault features is required. In this study, an adaptive variational modal decomposition (VMD) fault feature extraction method is proposed, utilizing the sparrow search algorithm (SSA). Firstly, a novel measurement index called impulse diversity entropy (IDE) is introduced, which better represents internal changes within the mode components. Secondly, the SSA is employed to select the optimal VMD decomposition parameters based on the IDE. Finally, a spectrum analysis is conducted on the mode component with the highest IDE to extract fault features. The experimental results show that this method has an accurate feature extraction ability and obvious advantages over other methods in distinguishing fault and interference frequencies because it is a special signal decomposition method.

Bearing fault diagnosis of rail vehicles traction motor based on adaptive periodic segment matrix and singular value decomposition

The early fault characteristics of rolling bearings are relatively weak and accompanied by large amounts of noise, resulting in the collected bearing vibration signal having an extremely low signal-to-noise ratio, hence creating challenges in diagnosing faults in rail vehicle bearings. This paper proposes a method based on an adaptive period segment matrix and singular value decomposition to extract fault information from bearing fault impulse signals, in response to the problem of unstable period recognition in existing rolling bearing fault diagnosis methods due to strong background noise. Firstly, a periodic segment estimation method based on the average envelope spectrum is proposed. By calculating the envelope spectrum of the vibration signal, a method is proposed for assessing period length and period constraint F in segments. Then this method is applied to pinpoint the fault impact peak and the position of the vibration signal. Utilizing the fault impact peak and the calculated period constraint F, a strict rank-1 adaptive period segment matrix (APSM) is introduced. Finally, singular value decomposition is performed on this matrix, the bearing impulse vibration signal can be reconstructed through the first maximum singular value. The simulation and experimental analysis results of rolling bearings signals demonstrate the performance of the proposed method, even under severe background noise conditions(−20 dB), and the calculated envelope spectrum signal-to-noise ratio further supports the adaptive periodic segment matrix has an excellent ability to identify and extract periodic components, thus validating its usefulness as a fault diagnosis approach for rolling bearings.

Restricted Sparse Networks for Rolling Bearing Fault Diagnosis

The application of deep learning-based rolling bearing fault diagnosis methods in high reliability scenarios is limited due to low transparency. In addition, the scaling up of the deep learning models, in order to improve the performance of rolling bearing fault diagnosis (RBFD), has led to difficulties in its application in low-resource scenarios. Based on these facts, a new neural network, restricted sparse networks (RSNs), is proposed in this work. Firstly, a restricted sparse frequency domain space (RSFDS) is proposed for the interpretable representation of rolling bearing fault features (RBFFs) based on the quadratic complex domain equation. Secondly, an interpretable multi-channel fusion mechanism is designed to map RBFFs to RSFDS. Furthermore, a high power feature extraction module is developed to extract RBFFs in an efficient and easy-to-understand manner. Finally, an end-to-end RBFD network is provided for high reliability and resource-constrained scenarios. The experimental results show that RSNs have favorable fault diagnosis accuracy performance that is parallel to the state-of-the-art methods. More importantly, the model size of the proposed network only accounts for 20%-30% of the conventional methods.

A Rolling Bearing Fault Feature Extraction Algorithm Based on IPOA-VMD and MOMEDA.

Since the rolling bearing fault signal captured by a vibration sensor contains a large amount of background noise, fault features cannot be accurately extracted. To address this problem, a rolling bearing fault feature extraction algorithm based on improved pelican optimization algorithm (IPOA)-variable modal decomposition (VMD) and multipoint optimal minimum entropy deconvolution adjustment (MOMEDA) methods is proposed. Firstly, the pelican optimization algorithm (POA) was improved using a reverse learning strategy for dimensional-by-dimensional lens imaging and circle mapping, and the optimization performance of IPOA was verified. Secondly, the kurtosis-square envelope Gini coefficient criterion was used to select the optimal modal components from the decomposed components of the signal, and MOMEDA was used to process the optimal modal components in order to obtain the optimal deconvolution signal. Finally, the Teager energy operator (TEO) was employed to demodulate and analyze the optimally deconvoluted signal in order to enhance the transient shock component of the original fault signal. The effectiveness of the proposed method was verified using simulated and actual signals. The results showed that the proposed method can accurately extract failure characteristics in the presence of strong background noise interference.

Early Fault Detection of Rolling Bearings Based on Time-Varying Filtering Empirical Mode Decomposition and Adaptive Multipoint Optimal Minimum Entropy Deconvolution Adjusted.

Due to the early formation of rolling bearing fault characteristics in an environment with strong background noise, the single use of the time-varying filtering empirical mode decomposition (TVFEMD) method is not effective for the extraction of fault characteristics. To solve this problem, a new method for early fault detection of rolling bearings is proposed, which combines multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) with parameter optimization and TVFEMD. Firstly, a new weighted envelope spectrum kurtosis index is constructed using the correlation coefficient and envelope spectrum kurtosis, which is used to identify the effective component and noise component of the bearing fault signal decomposed by TVFEMD, and the intrinsic mode function (IMF) containing rich fault information is selected for reconstruction. Then, a new synthetic impact index (SII) is constructed by combining the maximum value of the autocorrelation function and the kurtosis of the envelope spectrum. The SII index is used as the fitness function of the gray wolf optimization algorithm to optimize the fault period, T, and the filter length, L, of MOMDEA. The signal reconstructed by TVF-EMD undergoes adaptive filtering using the MOMEDA method after parameter optimization. Finally, an envelope spectrum analysis is performed on the signal filtered by the adaptive MOMEDA method to extract fault feature information. The experimental results of the simulated and measured signals indicate that this method can effectively extract early fault features of rolling bearings and has good reliability. Compared to the classical FSK, MCKD, and TVFEMD-MOMEDA methods, the first-order correlated kurtosis (FCK) and fault feature coefficient (FFC) of the filtered signal obtained using the proposed method are the largest, while the sample entropy (SE) and envelope spectrum entropy (ESE) are the smallest.

Ramanujan-gram: an autonomous weak period fault extraction method under strong noise

Under the influence of strong noise, period fault features of rolling bearing are not obvious, which increases the difficulty of accurately extracting period fault features. An autonomous weak period fault extraction method under strong noise named Ramanujan-gram is proposed in this paper. The greatest advantage of Ramanujan-gram is that it uses the Ramanujan feature extraction technique to reconstruct the components in each frequency band, which can overcome the weakness of the weak noise robustness of the filter methods used by the traditional kurtogram methods and improve the accuracy of period fault feature extraction. Meanwhile, the adaptive frequency band segmentation method based on the order statistical filter is used for adaptive frequency band segmentation, which overcomes the defect that the binary tree structure of fixed frequency band segmentation may destroy the optimal demodulated frequency band. Considering that kurtosis index is difficult to accurately evaluate period fault information in components, Ramanujan-gram adopts adaptive square envelope spectrum weighted kurtosis index to improve the evaluation accuracy of period fault information. The test signals of rolling bearing verify that Ramanujan-gram has strong noise robustness and is an effective method for weak period fault extraction under strong noise.

Low-rank and periodic group sparse based signal denoising method for rolling bearing fault feature extraction

Vibration signal analysis is a vital method of achieving rolling bearing fault feature extraction, but the fault impulses contained in the vibration signals are susceptible to interference from noise, making it difficult to extract fault features. In order to effectively extract the fault features of rolling bearings, an adaptive low-rank (LR) and periodic group sparse (AdaLRPGS) denoising method is proposed. Firstly, an AdaLRPGS model is constructed, which is not only an improvement of the classical sparse LR method, but also can effectively enhance the LR and periodic group sparsity of the failure impulses. Secondly, an adaptive period prior matching method is proposed to adaptively match the period of the fault impulses, and the moth flame optimization algorithm is utilized to adaptively search the regularization parameter of the AdaLRPGS model, and then the rule for determining the regularization parameter is summarized in the simulation analysis, which solves the dependence of the AdaLRPGS model on the period prior and regularization parameter. Finally, the solution procedure of the AdaLRPGS model is derived under the framework of the alternating direction method of multipliers. The simulated and measured signals are analyzed using the proposed method and compared with some advanced methods. The results show that the proposed method can extract the fault features of rolling bearings and has significant advantages compared with some advanced methods.

Use of Composite Multivariate Multiscale Permutation Fuzzy Entropy to Diagnose the Faults of Rolling Bearing.

The study focuses on the fault signals of rolling bearings, which are characterized by nonlinearity, periodic impact, and low signal-to-noise ratio. The advantages of entropy calculation in analyzing time series data were combined with the high calculation accuracy of Multiscale Fuzzy Entropy (MFE) and the strong noise resistance of Multiscale Permutation Entropy (MPE), a multivariate coarse-grained form was introduced, and the coarse-grained process was improved. The Composite Multivariate Multiscale Permutation Fuzzy Entropy (CMvMPFE) method was proposed to solve the problems of low accuracy, large entropy perturbation, and information loss in the calculation process of fault feature parameters. This method extracts the fault characteristics of rolling bearings more comprehensively and accurately. The CMvMPFE method was used to calculate the entropy value of the rolling bearing experimental fault data, and Support Vector Machine (SVM) was used for fault diagnosis analysis. By comparing with MPFE, the Composite Multiscale Permutation Fuzzy Entropy (CMPFE) and the Multivariate Multiscale Permutation Fuzzy Entropy (MvMPFE) methods, the results of the calculations show that the CMvMPFE method can extract rolling bearing fault characteristics more comprehensively and accurately, and it also has good robustness.

Rolling bearing fault diagnosis based on generalized multiscale mean permutation entropy and GWO‐LSSVM

AbstractFaults in rolling bearings are usually observed through pulses in the vibration signals. However, due to the influence of complex background noise and interference from other machine components present in measurement equipment, vibration signals are typically non‐stationary and often contaminated by noise. Therefore, in order to effectively extract the random variation and non‐linear dynamic variation characteristics of vibration signals, a new method of rolling bearing fault diagnosis based on generalized multiscale mean permutation entropy (GMMPE) and grey wolf optimized least squares support vector machine (GWO‐LSSVM) is put forward in this paper. Based on the multiscale permutation entropy (MPE), the multiscale equalization is firstly used to replace the coarse grained process, and the value of mean is extended to variance to avoid the dynamic mutation of the original signal. Finally, the parameters of LSSVM are optimized by the grey wolf optimization algorithm to achieve accurate identification of fault modes. The results of simulation and experiment show that applying the proposed GMMPE to rolling bearing fault feature extraction is feasible and superior, and the method based on GMMPE and GWO‐LSSVM has better noise robustness, which can effectively achieve rolling bearing fault diagnosis.

Mechanical Incipient Fault Detection and Performance Analysis Using Adaptive Teager-VMD Method

For large rotating machinery with low speed and heavy load, the incipient fault characteristics of rolling bearings are particularly weak, making it difficult to identify them effectively by direct signal processing methods. To resolve this issue, we propose a novel approach to detecting incipient fault features that combines signal energy enhancement and signal decomposition. First, the structure of a conventional Teager algorithm is modified to further increase the energy of the micro-impact component and hence the impact amplitude. Then, a kind of composite chaotic mapping is constructed to extend the original fruit fly optimization algorithm (FOA) framework, improving the FOA’s randomness and search power. The effective intrinsic mode functions (IMFs) are determined by searching for the optimal combination values of the key parameters of the variational mode decomposition (VMD) with the improved chaotic FOA (ICFOA). The kurtosis index is then used to select the IMFs that are most relevant to the fault characteristics information. Finally, the sensitive components are analyzed to identify multiple early fault characteristics and determine detailed information about the faults. Moreover, the approach is evaluated by a simulation signal and a measured signal. The comprehensive evaluation indicates that the approach has clear advantages over other excellent methods in extracting the incipient fault feature information of the equipment and has great potential for application in engineering.

The Single-channel blind source separation based on VMD and Tukey M estimation for rolling bearing composite fault diagnosis

Rolling bearing is one of the core components in rotating machinery, and its running status directly affects the operation of the whole equipment. Faults of rolling bearings in the actual working process are often multiple faults. To effectively separate fault sources, the blind source separation method is used for the compound fault diagnosis of rolling bearings. Because of the impact of the number of artificially limited decompositions and quadratic penalty factor on VMD in the decomposition process, and the slow convergence and low accuracy in the objective function of traditional FastICA operation, the VMD algorithm based on the energy loss coefficient and the information entropy is proposed, which adaptively determines the number of modal components and the quadratic penalty factor; The Tukey M estimation is selected as the objective convergence function of the FastICA algorithm to improve its robustness. First, VMD is used to decompose the signal; Secondly, the original signal and the decomposed IMF component are reconstructed, the covariance matrix and the singular value decomposition are constructed, the number of fault sources is estimated by the proximity dominance method, and the decomposed IMF components are filtered through correlation analysis and kurtosis index to build a multi-channel feature set; Finally, the constructed multi-channel feature set is input to the FastICA algorithm based on the Tukey M estimation for the separation of fault source signals to achieve composite fault diagnosis. The compound fault experiment shows that the proposed method in this paper can effectively realize the blind source separation of rolling bearing fault features to realize the compound fault diagnosis in different positions.

A time-varying instantaneous frequency fault features extraction method of rolling bearing under variable speed

Under variable speed, the time-frequency analysis (TFA) method for processing rolling bearing vibration signal is susceptible to noise, which cannot extract accurate time-frequency features. Plus, the special meaning of each time-frequency feature remains unclear. Based on the dynamic characteristics of rolling bearing and combined with the improved multisynchrosqueezing transform (IMSST), empirical Fourier decomposition (EFD), and generalized demodulation (GD), a time-varying instantaneous frequency fault features extraction method of rolling bearing under variable speed is proposed in this paper. Firstly, the instantaneous frequency (IF) trajectory of the rotating shaft is accurately represented by the better time-frequency resolution of IMSST, and the demodulation phase function for GD is constructed by combining the fault characteristic coefficient (FCC) of the rolling bearing. Then, the components in the signal that match the demodulation phase function are mapped into constant IF components. Through adaptive EFD and inverse demodulation, the time-varying IF components satisfying the conditions are separated. Finally, based on the Hilbert transform (HT) and adaptive time-frequency spectrum (ATFS), a high-quality time-frequency representation (TFR) is reconstructed to extract the time-varying IF fault features and identify the defect type of rolling bearing. The simulation and measured signal analysis results show that the proposed method can accurately extract fault features of rolling bearings with different defects under variable speed and identify fault types, thus maximally avoiding the influence of noise and interference components.

An improved decomposition method for extracting unknown rolling bearing fault features from strong noise

An improved decomposition method for extracting unknown rolling bearing fault features from strong noise