Types Of Bearing Faults Research Articles

Weak fault diagnosis of rolling bearing under variable speed condition using IEWT-based enhanced envelope order spectrum

In order to achieve status identification of rolling bearing at early fault stage under variable speed conditions, a method called IEWT-based enhanced envelope order spectrum is proposed by improving empirical wavelet transform (IEWT) using cuckoo search algorithm (CSA) and combing IEWT with the compute order tracking (COT) approach and singular value ratio spectrum (SVRS) denoising technology. The inherent deficiencies of traditional EWT limit its application in fault signal processing significantly. In order to address this issue, the support interval of an empirical wavelet is adaptively determined by CSA to ensure the accuracy of signal decomposition, and an IEWT method is proposed. For a given bearing fault signal under fluctuating speed conditions, firstly COT is utilized as a preprocessing technology to convert the original time domain signal into the resampled angle domain signal without special hardware. Then, IEWT is used to separate the informative component effectively from the resampled signal. Due to the weak features and the heavy noises in the early fault signal, IEWT is unable to remove the whole interference contained in the separated component directly. Thus, SVRS denoising technology is applied on the envelope signal of the separated signal to further enhance the potential impulsive features and suppress the residual noises. Finally, the fault type of rolling bearing can be determined by analyzing the obtained enhanced envelope order spectrum. Both the simulated and the experimental signals are used to verify the effectiveness of the proposed method, and the comparisons between the proposed method and the EEWT, the EMD, and the spectral kurtosis methods are carried out. The results indicate the proposed method possesses significant advantages in bearing weak fault diagnosis under variable speed condition.

Fault Diagnosis of Rolling Element Bearings with a Two-Step Scheme Based on Permutation Entropy and Random Forests.

This study presents a two-step fault diagnosis scheme combined with statistical classification and random forests-based classification for rolling element bearings. Considering the inequality of features sensitivity in different diagnosis steps, the proposed method utilizes permutation entropy and variational mode decomposition to depict vibration signals under single scale and multiscale. In the first step, the permutation entropy features on the single scale of original signals are extracted and the statistical classification model based on Chebyshev’s inequality is constructed to detect the faults with a preliminary acquaintance of the bearing condition. In the second step, vibration signals with fault conditions are firstly decomposed into a collection of intrinsic mode functions by using variational mode decomposition and then multiscale permutation entropy features derived from each mono-component are extracted to identify the specific fault types. In order to improve the classification ability of the characteristic data, the out-of-bag estimation of random forests is firstly employed to reelect and refine the original multiscale permutation entropy features. Then the refined features are considered as the input data to train the random forests-based classification model. Finally, the condition data of bearings with different fault conditions are employed to evaluate the performance of the proposed method. The results indicate that the proposed method can effectively identify the working conditions and fault types of rolling element bearings.

A Multi-Stage Hybrid Fault Diagnosis Approach for Rolling Element Bearing Under Various Working Conditions

To timely detect bearing operating condition, and accurately identify bearing fault type and fault severity, a novel multi-stage hybrid fault diagnosis strategy for rolling bearing is proposed in this paper, which mainly consists of three stages (i.e. fault initial detection, fault type recognition and fault severity assessment). Firstly, the procedure of permutation entropy (PE)-based fault initial detection is performed to estimate bearing operating condition. If the bearing fault exists, the next two stages are conducted for fault type recognition and fault severity assessment. Specifically, in the second and third stages, for each dataset under different fault conditions, hybrid-domain features including time-domain, frequency-domain and time-frequency domain are firstly extracted to establish high-dimensional feature space based on statistical analysis and variational mode decomposition (VMD). Then, locality preserving projection (LPP) is introduced to compress high-dimensional dataset into low-dimensional feature space which can reflect preferably intrinsic information of the raw signal and remove information redundancy embedded in hybrid-domain features. Finally, the obtained low-dimensional dataset is imported into Fuzzy C-means (FCM) clustering for recognizing bearing fault type and fault severity. The efficacy of the proposed approach is verified by experimental bearing data under different working conditions. The results indicate that our proposed method can both assess effectively bearing health status and recognize accurately bearing fault type and fault severity. In addition, our proposed approach has higher diagnosis precision than traditional single-stage diagnosis method mentioned in this paper.

ISVD-Based In-Band Noise Reduction Approach Combined With Envelope Order Analysis for Rolling Bearing Vibration Monitoring Under Varying Speed Conditions

In practical applications, the signal measured from a complex mechanical system is usually disturbed by various noises due to the compounded effect of interferences of other machine elements and background noises, especially under varying speed conditions. Resonance-based approaches have been proven to be effective methods to address this problem. However, even if the optimal resonance band is accurately determined, the in-band noise with frequency content in the range covered by the band-pass filter is not eliminated. To avoid missed diagnosis and misdiagnosis of faults in bearings, an iterated SVD (ISVD)-based in-band noise reduction method combined with envelope order spectrum analysis is proposed in this paper. First, the optimal frequency band of a vibrational signal is determined with the help of an enhanced wavelet packet transform kurtogram, in which the kurtosis of each node is calculated based on the envelope spectrum of a signal be reconstructed using the wavelet packet coefficients. The node with the maximum kurtosis value is used to reconstruct the signal. Second, the envelope of a reconstructed signal is calculated by Hilbert transform and the ISVD method is applied to it to reduce the in-band noise. To avoid the destruction of useful information caused by excessive iteration, a threshold is set to determine the number of iterations. After iterative processing, a de-noised signal is reconstructed based on the relationship between the singular value and a frequency component. Finally, the reconstructed signal is resampled and transformed into the fault characteristic order domain where the bearing fault type can be identified from the envelope order spectra. The simulations and experiments were used to validate the efficacy of the proposed method. Compared with the spectral subtraction method, the ISVD method can suppress in-band noise efficiently and beneficial to extract the fault characteristic order under variable speed conditions.

Multi-Bandwidth Mode Manifold for Fault Diagnosis of Rolling Bearings

Variational mode decomposition (VMD) has been widely applied to the field of machinery fault diagnosis due to its good time-frequency decomposition capability. However, the performance of the VMD for extraction of fault transient components is dependent on proper parameters in the method and is not satisfactory under noisy conditions. This paper proposes a new method, termed multi-bandwidth mode manifold (Triple M, TM), to extract the real fault transient components for fault diagnosis of rolling bearings. The new TM method unites multiple fault-related modes with different bandwidths by a nonlinear manifold learning algorithm. The modes with large bandwidths contain more fault-related information while those with small bandwidths contain less fault-unrelated components and noise. The advantages of different fault-related modes are fully used by the proposed method, leading to clear fault transients that facilitates the bearing fault detection. First, an efficiency-improved VMD method named recycling VMD (RVMD) is performed on the bearing signal repeatedly with different parameter values to obtain the fault-related modes with different bandwidths, i.e., the multi-bandwidth modes. Then, the manifold learning algorithm is carried out to extract the intrinsic manifold structure of bearing fault transient components from the multi-bandwidth modes. Finally, the bearing fault type is identified from the extracted feature with clear fault transients. The enhanced performance of the proposed method over the traditional methods is validated by a simulation analysis and two experimental applications of bearing defect identifications.

A Coarse TF Ridge-Guided Multi-Band Feature Extraction Method for Bearing Fault Diagnosis Under Varying Speed Conditions

Currently, most of the vibration signal analysis methods for bearing fault diagnosis under varying speed conditions are based on the resampling technology with shaft rotational frequency (SRF). However, the SRF obtained by a fixed tachometer or time-frequency (TF) ridge detection reduces measuring flexibility or introduces errors inevitably. In this paper, a multi-band feature extraction method using coarse TF ridge-guided variational nonlinear chirp mode decomposition (VNCMD) is proposed for bearing fault diagnosis under varying speed conditions. Specifically, the proposed method is conducted as follows. First, the low-frequency component (LFC) and resonance component are extracted by the low-pass filtering and the fast kurtogram method, respectively, to alleviate the noise interference. Second, the coarse TF ridges are identified by a tractable ridge estimation method that is based on the TF representation for preliminary selection of the initial instantaneous frequency. Third, the coarse TF ridge-guided VNCMD is constructed to track the SRF and instantaneous fault characteristic frequency (IFCF) from the envelope signals of the LFC and the resonance component, respectively. Finally, the characteristic frequency ratio is computed on the basis of the values of SRF and IFCF to determine the fault type of ball bearing without resampling. The simulation studies and experimental verifications confirm that the proposed method can accurately locate bearing defect types and outperforms some existing methods.

Rolling Bearing Fault Diagnosis Based on SVDP-Based Kurtogram and Iterative Autocorrelation of Teager Energy Operator

The emergence of periodic impacts in the vibration signal is considered as an essential sign of rolling bearing faults. Therefore, how to distinguish the periodic impact component from the interference components (e.g., the harmonics and noise) in the raw vibration signal is critical for detecting bearing faults. The kurtogram technique plays an essential role in the automatic selection of sub-component signals containing fault information. However, two significant shortcomings reduce its ability to detect early weak transients: 1) the decomposition accuracy of the filters used in kurtogram, i.e., short-time Fourier transform (STFT) and binary filter banks, is deficiency and 2) the detection ability of kurtosis to cyclic impact is insufficient. A singular value decomposition packet (SVDP)-based kurtogram is proposed to improve the kurtogram technique. More specifically, a novel parameter-less signal decomposition algorithm, termed SVDP, is employed as the filter for sub-components extraction. The L-kurtosis indicator is then introduced to replace the kurtosis indicator to select the optimal sub-component from the SVDP processing results. Moreover, a fault signature highlighting technique named iterative autocorrelation of Teager energy operator (TEO-IAC) is presented, and the TEO-IAC spectrum is adopted to replace the Hilbert envelope spectrum to detect the fault characteristic frequencies of the optimal sub-component to determine the fault types of bearings. Finally, the presented fault diagnosis framework based on the SVDP-based kurtogram and TEO-IAC is compared with the original kurtogram, improved kurtogram and autogram in simulated and experimental signals analysis, which demonstrates its validity and superiority for extracting weak fault features of bearings.

Order spectrogram visualization for rolling bearing fault detection under speed variation conditions

Abstract For rotating machinery condition monitoring and fault diagnosis under speed variation conditions, order tracking has been considered as a very powerful non-stationary vibration signal analysis method, when compared with the frequency analysis methods based on stationary assumption. However, the conventional order tracking methods require additional hardware to provide a phase reference signal. This constraint limits the conventional methods in industrial applications significantly. In order to further improve the applicability of the conventional order tracking methods, some tacho-less order tracking methods have been proposed in the past few years. Despite the tacho-less order tracking methods successfully getting rid of reference signal, the algorithms are very complex and the computational burden is increased correspondingly. To address the aforementioned shortcomings, a straightforward tacho-less order tracking method based on order spectrogram visualization is proposed in this paper. In the proposed method, a ridge extraction approach is used to estimate the instantaneous frequency of a certain rotating frequency harmonic. And then the vibration signal is resonance demodulated and the time-frequency distribution of the demodulated signal is obtained. A subsequent transform is conducted and the frequency axis of the time-frequency distribution is rescaled based on the estimated instantaneous frequency of rotating shaft. Then, an order spectrogram is constructed and thereby the non-stationary interference introduced by rotating speed fluctuation is suppressed. Finally, fault orders are uncovered and bearing fault type can be identified. The effectiveness of the proposed method has been validated by both simulated and experimental rolling bearing vibration signals. The results illustrate the improved features regarding previously developed tacho-less order tracking method in bearing diagnosis under speed variation conditions.

Research on an Adaptive Variational Mode Decomposition with Double Thresholds for Feature Extraction

A motor bearing system is a nonlinear dynamics system with nonlinear support stiffness. It is an asymmetry system, which plays an extremely important role in rotating machinery. In this paper, a center frequency method of double thresholds is proposed to improve the variational mode decomposition (VMD) method, then an adaptive VMD (called DTCFVMD) method is obtained to extract the fault feature. In the DTCFVMD method, a center frequency method of double thresholds is a symmetry method, which is used to determine the decomposed mode number of VMD according to the power spectrum of the signal. The proposed DTCFVMD method is used to decompose the nonlinear and non-stationary vibration signals of motor bearing in order to obtain a series of intrinsic mode functions (IMFs) under different scales. Then, the Hilbert transform is used to analyze the envelope of each mode component and calculate the power spectrum of each mode component. Finally, the power spectrum is used to extract the fault feature frequency for determining the fault type of the motor bearing. To test and verify the effectiveness of the DTCFVMD method, the actual fault vibration signal of the motor bearing is selected in here. The experimental results show that the center frequency method of double thresholds can effectively determine the mode number of the VMD method, and the proposed DTCFVMD method can accurately extract the clear time frequency characteristics of each mode component, and obtain the fault characteristics of characteristics; frequency, rotating frequency, and frequency doubling and so on.

Detection of Generalized-Roughness and Single-Point Bearing Faults Using Linear Prediction-Based Current Noise Cancellation

The dominant components in the stator current of a typical induction motor contain a substantial amount of information that is not related to bearing faults and can be considered as “ noise ” to bearing fault detection. The presence of the noise in the current signal creates the possibility of missed and false alarms. This paper develops a current noise cancellation method. When noise cancellation is used for recovering a desired signal, the distinguishing characteristics of both the desired signal and the noise need to be considered. Contrary to conventional current noise cancellation methods, “predictability” is considered as the distinguishing characteristic. Linear prediction theory with an optimum prediction order is applied to efficiently model the predictable components as noise. The developed method is able to efficiently cancel out the noise from the current signal. The information about the mechanical load level, the faulty part of the bearing, machine parameters, nameplate values, the current spectrum distribution, and the current data associated with the healthy bearing is not required in this method. Simulation and experimental results confirm the merits and effectiveness of the developed method to detect the bearing fault types through kurtosis and spectral analysis.

A narrowband envelope spectra fusion method for fault diagnosis of rolling element bearings

Narrowband amplitude demodulation is an effective tool for extracting characteristic features in the fault diagnosis of rolling element bearings. The quality of demodulation largely depends on the frequency band selected for the demodulation. Numerous criteria have been constructed to determine the optimal frequency band. However, independent frequency interferences and in-band noises in narrowband signals can greatly affect the values of the criteria, which may lead to an inaccurate result in locating the optimal band, in demodulating fault features, and finally in the fault detection. Inspired by the nonlocal means (NL-means) denoising method that has been widely used in image processing, this paper proposes a narrowband envelope spectra fusion (NESF) method to enhance fault features and suppress in-band noises before criterion calculation. The method suppresses in-band noises by averaging envelope spectra at neighboring narrow bands. Meanwhile, some minor improvements are made to the conventional narrowband envelope spectrum calculation method to enhance the similarity of the narrowband envelope spectra containing fault features, and finally optimize the fusion process. Then, sparsity values of these denoised envelope spectra, which can lower the impact of independent frequency interferences, are utilized to determine the optimal band and select the optimal envelope spectrum. Frequency signatures of the extracted envelope spectrum can be utilized to indicate the status and fault types of rolling element bearings. A simulated bearing fault signal and three real bearing fault signals are used to validate the effectiveness of the proposed method through comparison studies with protrugram and sparsogram. The results show that the proposed method can effectively extract fault characteristics, even in a harsh environment.

Fault Detection Method for Rolling Mechanical Equipment Based on EEMD and RF

Since rolling mechanical equipment is critical to manufacture systems, fault detection and diagnosis for them become more important and urgent with the improving need for safely and economical running. Aiming at streaming vibration signals, a fault detection and diagnosis scheme is proposed with an off-line training stage and on-line detecting stage. After de-noising by wavelet transformation, signals are decomposed into several IMF (Intrinsic Mode Functions) by EEMD (Ensemble Empirical Mode Decomposition) method. Fault features are expressed on definite IMFs rather than in a mixed mode. Based on these IMFs, the fault types of bearings can be represented by PSD (Power Spectral Density) to get good identification ability, and even to get the detailed physical meaning. The RF (Random Forest) method is used for classification of various fault positions and degrees with high efficiency and accuracy. Simulations with real data show the practicability of the fault detection scheme, and the earlier fault detection or fault forecast can be achieved by expanding classification training set for mixed fault data.

Research on bearing fault feature extraction based on singular value decomposition and optimized frequency band entropy

Abstract Singular value decomposition (SVD) is widely used in condition monitoring of modern machine for its unique advantages. A novel relative change rate of singular value kurtosis (SVK) is proposed in order to determine the reconstructed order of singular values effectively. Since the bandwidth parameter of the band-pass filter designed by FBE need to be determined based on experience, obviously, there are significant deficiencies. Then, a optimized frequency band entropy (OFBE) method based on the principle of maximum kurtosis is proposed to optimize the bandwidth parameters. In addition, because the fault signal of the rolling bearing at the initial stage is very weak and submerged by ambient noise, SVD cannot extract fault features clearly, a new method for fault feature extraction of rolling bearing based on SVD and OFBE, named SVD-SVK-OFBE, is proposed. Firstly, the Hankel matrix is reconstructed from the original vibration signal in the phase space and the noise reduction is performed using SVD. Here, the relative change rate of singular value kurtosis is performed on the Hankel matrix to determine the reconstructed order. Secondly, the OFBE analysis is performed on the reconstructed signal to determine the center frequency and the bandwidth of the band-pass filter adaptively. The bandwidth of the designed band-pass filter is optimized by the kurtosis maximum principle. Thirdly, the reconstructed signal of SVD is filtered by the optimized filter, and the envelope demodulation analysis is performed on the filtered signal. Finally, the fault feature frequency is extracted and compared with the theoretical fault feature frequency to identify the fault type of the rolling bearing. The effectiveness and advantages of the method described in this paper are verified by the simulation analysis and experimental data analysis of the rolling bearing.

A novel deep output kernel learning method for bearing fault structural diagnosis

Abstract In recent years, machine learning techniques have been proved a promising tool for bearing fault diagnosis. However, in the traditional machine learning-based diagnosis methods, the fault features tend to be relatively simple and couldn’t work well for different fault type once a specific feature extraction method is determined. Meanwhile, although deep learning techniques can adaptively extract more representative features from bearing fault data, they are generally computationally expensive with slow convergence speed. Even if some deep learning algorithms like Multi-Layer Extreme Learning Machine (ML-ELM) can get fast training speed by means of non-tuned training strategy, they are inevitably of randomness to some extents. To solve this problem, a new deep learning method called deep output kernel learning is proposed in this paper to conduct collaborative diagnosis of multiple bearing fault types. The initial motivation is using the structural domain information among multiple bearing fault types to improve the diagnosis model’s generalization ability and robustness. By adopting ML-ELM as baseline algorithm, this paper firstly utilizes autoencoder to adaptively extract deep features, and then uses them to construct an objective function with output kernel regularizer. Finally, after solving this optimization problem, an output kernel matrix is obtained, and with this matrix, the final diagnosis model is built by fusing the multiple outputs of fault classifier. Experimental results on CWRU and IMS bearing data sets show that, compared to one state-of-the-art signal analysis method and eight typical machine learning-based diagnosis methods including four shallow learning algorithms and four deep learning algorithms, the proposed method can effectively improve the accuracy of bearing fault diagnosis in an acceptable time. Moreover, the results from the Kruskal-Wallis Test also indicate the proposed method has good numerical stability.

Bearing Health Diagnosed with a Mobile Phone: Acoustic Signal Measurements Can be Used to Test for Structural Faults in Motors

According to industry statistics, bearings are the components of low-voltage motors that fail most often. At the same time, the diagnostics of rollingelement bearings constitute a well-established component of the rotating machinery condition-monitoring domain. In many cases, however, the cost of installing a high-end, accelerometer-based bearing condition-monitoring system, which is the most common approach in the industry, may be difficult to justify for noncritical machinery due to the amount of time needed to see a return on this investment. This article examines the potential benefits of using modern, off-the-shelf mobile phones for recording acoustic signals originating from elements of rotating machines to monitor the conditions of rolling-element bearings. The main difficulty in using embedded mobile phone microphones for rotating machinery diagnostic purposes is that the frequency response of the mobile phone's microphone is very poor, i.e., below 200 Hz. The results presented in this article indicate that, with an appropriate signal processing approach, it is possible to detect the presence of faults in the bearings. More specifically, mobile-phone-based sound measurements may contain sufficient information that can not only distinguish between healthy and faulty cases but also determine the specific bearing fault type.

Recurrent Neural Network (RNN) Based Bearing Fault Classification of Induction Motor Employed in Home Water Pump System

In home appliances, the water pump is used to supply the water from a room to the other rooms. Defects of the waterpump are not distributing the water, inner stator winding short circuit, and bearing failure. In this paper, bearingfault detection of induction motor (IM) used in home water pump system is developed by using recurrent neuralnetwork (RNN) method. It is difficult to detect fault bearing of IM using a mathematical model. So that, a recurrentneural network (RNN) method is applied to solves this problem. These bearing faults classifications are based on IMstator current waveform. Bearing fault types are all normal (AN), front fault (FF), rear fault (RF), and all fault (AF).While, the detection process consist of three step. They are taking bearing fault data, features extraction, and RNNfault detection. The bearing fault data is taken from the stator currents of IM by using soundcard oscilloscopesoftware. Second step is features extraction process to obtain more bearing fault signs. In this step, stator currentsof IM is converted from time domain into frequency domain by using Fast Fourier Transform (FFT). Last stage isRNN model to clasify the bearing fault of IM. The effectiveness of proposed RNN method is clarified by using fourbearing fault types.

Early fault diagnosis of rolling bearings based on hierarchical symbol dynamic entropy and binary tree support vector machine

Early fault diagnosis of rolling bearings is crucial to operating and maintenance cost reduction of the equipment with bearings. This paper aims to propose a novel early fault feature extraction method based on the proposed hierarchical symbol dynamic entropy (HSDE) and the binary tree support vector machine (BT-SVM). Multiscale symbolic dynamic entropy (MSDE) has been recently proposed to characterize the dynamical behavior of time series. MSDE has several merits comparing with multiscale sample entropy (MSE) and multiscale permutation entropy (MPE), such as high computational efficiency and robustness to noise. However, MSDE only utilizes the fault information in the low frequency components and consequently the fault information hidden in the high frequency components is discarded. To address this shortcoming, a new method, namely HSDE, is proposed to extract the fault information in the high frequency components. Then, the BT-SVM is utilized to automatically complete the fault type identification. The effectiveness of the proposed method is validated using simulated and experimental vibration signals. Meanwhile, a comparison is conducted between MPE, hierarchical permutation entropy (HPE), MSE, hierarchical sample entropy (HSE), MSDE and HSDE. Results show that the proposed method performs best to recognize the early fault types of rolling bearings.

WPD and DE/BBO-RBFNN for solution of rolling bearing fault diagnosis

Abstract A new system of fault diagnosis which combines wavelet packet decomposition (WPD), radical basis function neural network (RBFNN) and a hybrid differential evolution with biogeography-based optimization (DE/BBO) algorithm is designed in this paper to improve the efficiency and accuracy of rolling bearing fault diagnosis. Biogeography-based optimization (BBO) is a new biogeography inspired algorithm, however it has the disadvantages of early-maturing and instable convergence. Combining BBO with differential evolution (DE) and making small modifications for the mutation strategy of DE, an improved DE/BBO algorithm is formed. Firstly, the fault feature vectors of original rolling bearing fault signals are effectively obtained by wavelet packet decomposition and reconstruction. Secondly, the RBF neural network is optimized by DE/BBO algorithm, the fault types of rolling bearing are trained and diagnosed next. The result of MATLAB simulation shows that in contrast to the traditional RBFNN, the fitness and precision of bearing fault diagnosis are higher and the root-mean-square error (RMSE) is lower because of the introduction of DE/BBO algorithm.

Fault diagnosis of wind turbine bearing based on stochastic subspace identification and multi-kernel support vector machine

In order to accurately identify a bearing fault on a wind turbine, a novel fault diagnosis method based on stochastic subspace identification (SSI) and multi-kernel support vector machine (MSVM) is proposed. First, the collected vibration signal of the wind turbine bearing is processed by the SSI method to extract fault feature vectors. Then, the MSVM is constructed based on Gauss kernel support vector machine (SVM) and polynomial kernel SVM. Finally, fault feature vectors which indicate the condition of the wind turbine bearing are inputted to the MSVM for fault pattern recognition. The results indicate that the SSI-MSVM method is effective in fault diagnosis for a wind turbine bearing and can successfully identify fault types of bearing and achieve higher diagnostic accuracy than that of K-means clustering, fuzzy means clustering and traditional SVM.

Comparison of Support Vector Machine‐Based Techniques for Detection of Bearing Faults

This paper presents a method that combines Shuffled Frog Leaping Algorithm (SFLA) with Support Vector Machine (SVM) method in order to identify the fault types of rolling bearing in the gearbox. The proposed method improves the accuracy of fault diagnosis identification after processing the collected vibration signals through wavelet threshold denoising. The global optimization and high computational efficiency of SFLA are applied to the SVM model. Simulation results show that the SFLA‐SVM algorithm is effective in fault diagnosis. Compared with SVM and Particle Swarm Optimization SVM (PSO‐SVM) algorithms, it is demonstrated that the SFLA‐SVM algorithm has the advantages of better global optimization, higher accuracy, and better reliability of diagnosis. Its accuracy is further improved through the integration of the wavelet threshold denoising method.