Rolling Bearing Fault Feature Research Articles

Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings

The incipient fault detection technology of rolling bearings is the key to ensure its normal operation and is of great significance for most industrial processes. However, the vibration signals of rolling bearings are a set of time series with non-linear and timing correlation, and weak incipient fault characteristics of rolling bearings bring about obstructions for the fault detection. This paper proposes a nonlinear dynamic incipient fault detection method for rolling bearings to solve these problems. The kernel function and the moving window algorithm are used to establish a non-linear dynamic model, and the real-time characteristics of the system are obtained. At the same time, the deep decomposition method is used to extract weak fault characteristics under the strong noise, and the incipient failures of rolling bearings are detected. Finally, the validity and feasibility of the scheme are verified by two simulation experiments. Experimental results show that the fault detection rate based on the proposed method is higher than 85% for incipient fault of rolling bearings, and the detection delay is almost zero. Compared with the detection performance of traditional methods, the proposed nonlinear dynamic incipient fault detection method is of better accuracy and applicability.

GMPSO-VMD Algorithm and Its Application to Rolling Bearing Fault Feature Extraction.

The vibration signal of an early rolling bearing is nonstationary and nonlinear, and the fault signal is weak and difficult to extract. To address this problem, this paper proposes a genetic mutation particle swarm optimization variational mode decomposition (GMPSO-VMD) algorithm and applies it to rolling bearing vibration signal fault feature extraction. Firstly, the minimum envelope entropy is used as the objective function of the GMPSO to find the optimal parameter combination of the VMD algorithm. Then, the optimized VMD algorithm is used to decompose the vibration signal of the rolling bearing and several intrinsic mode functions (IMFs) are obtained. The envelope spectrum analysis of GMPSO-VMD decomposed rolling bearing fault signal IMF1 was carried out. Moreover, the feature frequency of the four fault states of the rolling bearing are extracted accurately. Finally, the GMPSO-VMD algorithm is utilized to analyze the simulation signal and rolling bearing fault vibration signal. The effectiveness of the GMPSO-VMD algorithm is verified by comparing it with the fixed parameter VMD (FP-VMD) algorithm, complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) algorithm and empirical mode decomposition (EMD) algorithm.

Fault diagnosis for rolling bearing based on VMD-FRFT

In order to solve the problems that the fault feature of rolling bearing in early failure period is difficult to extract and the over-decomposition problem arises when applying the variational mode decomposition (VMD), in this paper, the fault diagnosis for rolling bearing based on variational mode decomposition–fractional Fourier transform (VMD-FRFT) is proposed. Firstly, based on the theory of fractional Fourier transform parameter estimation, the definition and derivation of VMD-FRFT are given and the modal component and center frequency estimation of the method are obtained. Secondly, the multicomponent linear frequency modulation (LFM) signal is simulated to verify the non-fragile to the center frequency distribution, the insensitive to the number of modes and the noise robustness of VMD-FRFT. Finally, VMD-FRFT is applied to the fault diagnosis of uniform acceleration rolling bearings, which can provide an effective filtering algorithm for the extraction of fundamental frequency and frequency multiplication of instantaneous frequency. Therefore, the VMD-FRFT proposed in this paper has certain reference significance for the fault diagnosis of rolling bearings.

Elimination of End effects in LMD Based on LSTM Network and Applications for Rolling Bearing Fault Feature Extraction

Local mean decomposition (LMD) is widely used in the area of multicomponents signal processing and fault diagnosis. One of the major problems is end effects, which distort the decomposed waveform at each end of the analyzed signal and influence feature frequency. In order to solve this problem, this paper proposes a novel self-adaptive waveform point extended method based on long short-term memory (LSTM) network. First, based on existing signal points, the LSTM network parameters of right and left ends are trained; then, these parameters are used to extend the waveform point at each end-side of signal; furthermore, the corresponding parameters are adaptively updated. The proposed method is compared with the characteristic segment extension and the traditional neural network extension methods through a simulated signal to verify the effectiveness. By combing the proposed method with LMD, an improved LMD algorithm is obtained. Finally, application of rolling bearing fault signal is carried out by the improved LMD algorithm, and the results show that the feature frequencies of the rolling bearing’s ball and inner and outer rings are successfully extracted.

Research on fault feature extraction method of rolling bearing based on improved wavelet threshold and CEEMD

Because bearing fault feature information is not easy to extract in noisy background, this paper proposes an improved rolling bearing fault feature extraction method based on combination of wavelet threshold and complementary ensemble empirical mode decomposition (CEEMD). Firstly, the improved wavelet threshold denoising method is used to reduce the noise of the vibration signal, and the impact characteristics in the signal are enhanced. Then, the CEEMD decomposition is used to obtain a set of Intrinsic Modal Functions (IMFs), and IMFs with larger kurtosis and correlation coefficients are selected for signal reconstruction. At last, the envelope spectrum analysis of reconstructed signal is carried out to extract fault characteristic information. Through the experimental analysis of the vibration signals of the outer ring and inner ring of the rolling bearing, it is proved that this method can effectively extract the fault characteristics of the bearing.

Improved multi-scale entropy and it's application in rolling bearing fault feature extraction

Multi-scale entropy has long been considered as a popular tool for measuring the complexity or detecting the nonlinear dynamic changes of time series, and can be utilized effectively to extract fault features from rolling bearing vibration signals. Nevertheless, with regard to information aliasing, which produces unreliable measurement information, it has been found to consistently exist in the coarse-graining process of multi-scale entropy due to the reduction of data points. In this paper, the adverse information aliasing phenomenon is analyzed, and subsequently two novel nonlinear dynamic methods called local maximum multi-scale entropy and extended multi-scale entropy are proposed. In the proposed methods, the coarse-graining process is endowed with clear physical meaning. Experimental results indicate that the proposed multi-scale analysis methods are suitable for both sample and permutation entropy. Compared with the original methods, the methods recommended in this paper hold better determination, stability and wider analysis scale, while being able tocharacterize the rolling bearing vibration signal more effectively.

A deep adaptive learning method for rolling bearing fault diagnosis using immunity

The extraction of rolling bearing fault features using traditional diagnostic methods is not sufficiently comprehensive and the features are often chosen subjectively and depend on human experience. In this paper, an improved deep convolutional process is used to extract a set of features adaptively. The hidden multi-layer feature of deep convolutional neural networks is also exploited to improve the extraction features. A deterministic detection of low-confidence samples is performed to ensure the reliability of the recognition results and to decrease the rate of false positives by evaluating the diagnosis of the deep convolutional neural network. To improve the efficiency of the continuous learning elements of the rolling bearing fault diagnosis, a clone learning strategy based on cloning and mutation operations is proposed. The experimental results show that the proposed deep convolutional neural network model can extract multiple rolling bearing fault features, improve classification and detection accuracy by reducing the false positive rate when diagnosing rolling bearing faults, and accelerate learning efficiency when using low-confidence rolling bearing fault samples.

Fault Diagnosis of Rolling Bearing Using Multiscale Amplitude-Aware Permutation Entropy and Random Forest

A rolling bearing is an important connecting part between rotating machines. It is susceptible to mechanical stress and wear, which affect the running state of bearings. In order to effectively identify the fault types and analyze the fault severity of rolling bearings, a rolling bearing fault diagnosis method based on multiscale amplitude-aware permutation entropy (MAAPE) and random forest is proposed in this paper. The vibration signals of rolling bearings to be analyzed are decomposed into different coarse-grained time series by using the coarse-graining procedure in multiscale entropy, highlighting the fault dynamic characteristics of vibration signals at different scales. The fault features contained in the coarse-grained time series at different time scales are extracted by using amplitude-aware permutation entropy’s sensitive characteristics to signal amplitude and frequency changes to form fault feature vectors. The fault feature vector set is used to establish the random forest multi-classifier, and the fault type identification and fault severity analysis of rolling bearings is realized through random forest. In order to demonstrate the feasibility and effectiveness of the proposed method, experiments were fully conducted in this paper. The experimental results show that multiscale amplitude-aware permutation entropy can effectively extract fault features of rolling bearings from vibration signals, and the extracted feature vectors have high separability. Compared with other rolling bearing fault diagnosis methods, the proposed method not only has higher fault type identification accuracy, but also can analyze the fault severity of rolling bearings to some extent. The identification accuracy of four fault types is up to 96.0% and the fault recognition accuracy under different fault severity reached 92.8%.

Incipient fault diagnosis of rolling bearings based on adaptive variational mode decomposition and Teager energy operator

Extracting incipient fault features of rolling bearings is a hard work as the impact compositions in vibration signals are faint and disturbed by a lot of environmental noise. An adaptive variational mode decomposition and Teager energy operator method (AVMD-TEO) for diagnosing incipient fault of rolling bearings is proposed. Firstly, the minimum average envelope entropy is used as the fitness value to search the optimal parameters of VMD adaptively by grey wolf optimization algorithm. Subsequently, the efficient weighted kurtosis index is introduced to select the efficient modal components for signal reconstruction. Finally, the reconstructed signal is processed by Teager energy operator to enhance the faint transient impact compositions and identify the defect frequency. The superiority of AVMD in parameters selection compared with fixed-parameter VMD and maximum weighted kurtosis optimized VMD is verified by simulated signal analysis. Results from the cases show that the peak signal-to-noise ratio and fault characteristic coefficient obtained by the proposed method are increased by 8% to 229% and 37% to 258% respectively compared with some traditional methods. The proposed AVMD-TEO can effectively reduce signal noise and extract incipient fault feature of rolling bearings.

An optimal variational mode decomposition for rolling bearing fault feature extraction

Rolling bearings usually work in tough conditions, which makes the collected vibration signals complex and the fault features weak. Hence, fault feature extraction methods for rolling bearings have become a research focus. In this paper, a new method termed optimal variational mode decomposition (VMD) is proposed to extract rolling bearing fault features. Firstly, since envelope entropy is very sensitive to fault signal features, envelope entropy is used as a fitness function, which is an objective function for the whale optimization algorithm (WOA). Secondly, the WOA has numerous merits, such as simple operation, fewer adjustment parameters and a strong ability for jumping out of the local optimum, and it is applied to the optimization of VMD. Finally, intrinsic mode function components are processed through a Teager energy operator. The proposed method is employed to analyze the experimental signal collected from rolling bearings. The comparison results show that the proposed method is more effective and demonstrates superiority over empirical mode decomposition, local mean decomposition and wavelet packet decomposition.

MVMD-MOMEDA-TEO Model and Its Application in Feature Extraction for Rolling Bearings.

In order to extract fault features of rolling bearings to characterize their operation state effectively, an improved method, based on modified variational mode decomposition (MVMD) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA), is proposed. Firstly, the MVMD method is introduced to decompose the vibration signal into intrinsic mode functions (IMFs), and then calculate the energy ratio of each IMF component. The IMF component is selected as the effective component from high energy ratio to low in turn until the total energy proportion Esum(t) ≥ 90%. The IMF effective components are reconstructed to obtain the subsequent analysis signal x_new(t). Secondly, the MOMEDA method is introduced to analyze x_new(t), extract the fault period impulse component x_cov(t), which is submerged by noise, and demodulate the signal x_cov(t) by Teager energy operator demodulation (TEO) to calculate Teager energy spectrum. Thirdly, matching the dominant frequency in the spectrum with the fault characteristic frequency of rolling bearings, the fault feature extraction of rolling bearings are completed. Finally, the experiments have compared MVMD-MOEDA-TEO with MVMD-TEO and MOMEDA-TEO based on two different data sets to verify the superiority of the proposed method. The experimental results show that MVMD-MOMEDA-TEO method has better performance than the other two methods, and provides a new solution for condition monitoring and fault diagnosis of rolling bearings.

Rolling Bearing Fault Diagnosis Algorithm Based on FMCNN-Sparse Representation

The time-frequency analysis of vibration signals is an effective means to analyze the fault characteristics of rolling bearings. The traditional pattern recognition method is difficult to adapt to the complex mapping relationship between the high-dimensional feature space and the state space. The deep learning method has high-dimensional feature adaptive analysis ability, which is suitable for the intelligent analysis of the high-dimensional feature space in fault states. The feedforward deep convolutional neural network (CNN) has achieved some success in mechanical fault diagnosis. However, the rolling bearing fault signal is complex, and there are many interference factors. The CNN relying on the simple feedforward method cannot effectively meet the actual needs in the field of fault diagnosis. Although there are some CNNs with feedback methods, the CNNs of these feedback methods cannot systematically obtain the characteristic information of rolling bearing faults. Therefore, they do not solve the feature extraction problem of rolling bearing faults well. In view of this, this paper provides a specific mathematical definition of the feedback mechanism for constructing the feedback mechanism in the deep CNN, models the feedback mechanism into an optimization problem, determines the basic framework of the feedback mechanism, and an effective feedback mechanism calculation model is proposed. Based on this, a solution algorithm based on the gradient descent method is proposed. Then, an effective supervised feature extraction method based on sparse expression is proposed. It maps the sample features to the feature domain through the effective transform method. In the process, the wavelet packet transform (WPT) transform is used as the basis function to construct a dictionary with structural effects, and mixed penalty terms are introduced to further optimize the performance of structural sparse expression. Finally, the sparse expression is combined with the feedback mechanism CNN (FCNN) to establish a sub-module fault diagnosis network so that a diagnosis can determine the fault severity while assessing the bearing fault location. The example shows that the method proposed in this paper has high accuracy in determining the state of rolling bearings and has great application potential in engineering.

基于小波包降噪与VMD的滚动轴承故障特征提取方法

基于小波包降噪与VMD的滚动轴承故障特征提取方法

Research on Rolling Bearing Fault Feature Extraction Based on Singular Value Decomposition considering the Singular Component Accumulative Effect and Teager Energy Operator

The extraction of impulsive signatures from a vibration signal is vital for fault diagnosis of rolling element bearings, which are always whelmed by noise, especially in the early stage of defect development. Aiming at the weak defect diagnosis, kurtosis of Teager energy operator (KTEO) spectrum is employed to indicate the fault information capacity of a spectrum, and considering the accumulative effect of a singular component, accumulative kurtosis of TEO (AKTEO) is firstly proposed to determine the proper signal reconstructed order during vibration signal processing using singular value decomposition (SVD). Then, a vibration processing scheme named SVD‐AKTEO is designed where an iteration is employed to reflect an accumulative singular effect by kurtosis of TEO spectrum. Finally, the fault diagnosis results can be extracted from the TEO spectrum output by SVD‐AKTEO. Simulation data and real data from a run‐to‐failure experiment of a rolling bearing are adopted to validate the efficiency, and comparative analysis demonstrates the feasibility to detect the early defect of the rolling bearing.

Sharing pattern feature selection using multiple improved genetic algorithms and its application in bearing fault diagnosis

In order to select the effective features or feature subsets and realize an intelligent diagnosis of aero engine rolling bearing faults, this paper presents a sharing pattern feature selection method using multiple improved genetic algorithms. Based on the simple genetic algorithm, a multiple-population improved genetic algorithm was proposed, which improves the speed and effect of algorithm and overcomes the shortcomings of local optima that simple genetic algorithm is easy to fall into. Because all populations regularly share and exchange their selecting features, the proposed algorithms can quickly dig up the current effective feature patterns, and then analyze and deal with the strong correlation between the feature patterns. This will not only give clear directions for the descendant evolution, but also help to achieve high accuracy feature selection, for, the features are highly distinctive. This multiple-population improved genetic algorithm was applied to rolling bearing fault feature selection and comparisons with other methods are carried out, which demonstrates the validity of sharing pattern feature selection method proposed.

Incipient fault detection of rolling bearing using maximum autocorrelation impulse harmonic to noise deconvolution and parameter optimized fast EEMD

Incipient Fault Detection of Rolling Bearing with heavy background noise and interference harmonics is a hot topic. In this paper, a new method based on parameter optimized fast EEMD (FEEMD) and Maximum Autocorrelation Impulse Harmonic to Noise Deconvolution (MAIHND) method is proposed for detecting the incipient fault of rolling bearing. Firstly, the FEEMD method with parameters optimization is used to reduce the noise and eliminate the interference harmonics of the fault signal. As a noise assistant improved method, the FEEMD can reduce the mode mixing and enhance the calculation efficiency significantly. Secondly, a new indicator is developed to select the sensitive IMF. Finally, a novel MAIHND method is employed to extract impulse fault feature from the sensitive IMF. Simulation and experiments results indicated that the proposed parameter optimized FEEMD–MAIHND method can effectively identify the weak impulse fault feature of rolling bearing. Moreover, the excellent performance of the proposed indicator for sensitive IMF component selection and MAIHND method is verified.

A Novel Fault Diagnosis Method for Rolling Bearing Based on Improved Sparse Regularization via Convex Optimization

Structural health monitoring and fault state identification of key components, such as rolling bearing, located in the mechanical main drive system, have a vital significance. The acquired fault signal of rolling bearing always presents the obvious nonlinear and nonstationary characteristics. Moreover, the concerned features are submerged in strong background noise. To handle this difficulty, a novel fault signal denoising scheme based on improved sparse regularization via convex optimization is proposed to extract the fault feature of rolling bearing. In this paper, the generalized minimax-concave (GMC) penalty is firstly researched to promote the sparsity of signal, which is based on traditional L1-norm and Huber function. It is designed to estimate the sparse solutions more accurately and maintain the convexity of the cost function. Then, the GMC penalty is extended to 1-D first-order total variation (TV) as nonseparability and nonconvex regularizer. Thus, a convex optimization problem, which involves a quadratic data fidelity term and a convex regularization term, is developed in this paper. To accelerate the convergence of the algorithm, it is solved by forward-backward (FB) iterative algorithm and thus the denoised signal can be obtained. In order to demonstrate its performance, the proposed method is illustrated for numerical simulation signal and applied in the feature extraction of the measured rolling bearing vibration signal.

An improved stochastic resonance method with arbitrary stable-state matching in underdamped nonlinear systems with a periodic potential for incipient bearing fault diagnosis

Rolling element bearings are critical mechanical parts that are prone to damage, and the detection of their incipient faults plays an important role in ensuring the safe and reliable operation of rotating machinery. The incipient fault characteristics of rolling bearings suffer attenuation from complicated transmission paths and, moreover, are overwhelmed by background noise, hence it is a challenging task to extract them from a complex environment of vibration signals. In contrast to traditional signal processing methods, stochastic resonance (SR) methods can utilize the noise to highlight incipient fault characteristics. However, most overdamped SR methods can hardly suppress multiscale noise, and the monostable, bistable and even tristable SR methods can hardly achieve arbitrary stable-state matching between various mechanical vibration signals and stable-state types. Combined with genetic algorithms (GAs) and the fourth-order Runge–Kutta algorithm to simultaneously obtain the optimal system parameter, damping factor and damping factor of the new SR model, an improved underdamped periodic SR (UPSR) method with arbitrary stable-state matching in underdamped multistable nonlinear systems with a periodic potential for incipient bearing fault diagnosis is proposed. The periodic potential can achieve the matching between various vibration signals and arbitrary stable-state types and, moreover, underdamped SR can suppress the multiscale noise. To improve the performance in bearing fault detection, the signals in the actual engineering environment are preprocessed by prewhitening processing and a Hilbert transform. Therefore, the improved UPSR method is expected to possess a good ability for extracting incipient fault characteristics. Both simulated and experimental comparison with the underdamped bistable SR (UBSR) and fast-Kurtogram methods are adopted to verify the effectiveness of the proposed method. Compared with the above two methods, the proposed method has better fault characteristic frequency extraction performance. The results show that the proposed method could be more suitable and widely used for incipient bearing fault diagnosis in background noise.

Incipient fault feature extraction of rolling bearings based on the MVMD and Teager energy operator

Aiming at the problems that the incipient fault of rolling bearings is difficult to recognize and the number of intrinsic mode functions (IMFs) decomposed by variational mode decomposition (VMD) must be set in advance and can not be adaptively selected, taking full advantages of the adaptive segmentation of scale spectrum and Teager energy operator (TEO) demodulation, a new method for early fault feature extraction of rolling bearings based on the modified VMD and Teager energy operator (MVMD-TEO) is proposed. Firstly, the vibration signal of rolling bearings is analyzed by adaptive scale space spectrum segmentation to obtain the spectrum segmentation support boundary, and then the number K of IMFs decomposed by VMD is adaptively determined. Secondly, the original vibration signal is adaptively decomposed into K IMFs, and the effective IMF components are extracted based on the correlation coefficient criterion. Finally, the Teager energy spectrum of the reconstructed signal of the effective IMF components is calculated by the TEO, and then the early fault features of rolling bearings are extracted to realize the fault identification and location. Comparative experiments of the proposed method and the existing fault feature extraction method based on Local Mean Decomposition and Teager energy operator (LMD-TEO) have been implemented using experimental data-sets and a measured data-set. The results of comparative experiments in three application cases show that the presented method can achieve a fairly or slightly better performance than LMD-TEO method, and the validity and feasibility of the proposed method are proved.

An early fault feature extraction method for rolling bearings based on variational mode decomposition and random decrement technique

The early fault characteristics of rolling bearing are weak, and the background noise is so strong that it is difficult to diagnose. In order to solve the above problems, an early fault feature extraction method for rolling bearings based on variational mode decomposition and random decrement technique was proposed. The variational mode decomposition was used to decompose the collected vibration signals, and the component with the larger correlation coefficient was selected as the fault component. Then the fault component was processed by random decrement technology, and the Hilbert envelope spectrum of the fault component was made. According to the proposed method, the early fault characteristic of outer ring of rolling bearing was extracted. Compared with the method based on EMD, the proposed method is more effective in extracting the early fault characteristics of rolling bearings.