Compound Fault Diagnosis Of Rolling Bearing Research Articles

Rolling bearing compound fault diagnosis based on spatiotemporal intrinsic mode decomposition

Aiming at the vibration signal characteristics of multi-channel rolling bearing complex faults containing various shock components, a rolling bearing complex fault diagnosis model based on spatiotemporal intrinsic mode decomposition (STIMD) method and fast spectral kurtosis method was proposed. The spatiotemporal intrinsic mode decomposition method combines the signal atomic decomposition method with the idea of signal blind source separation. Through the fast independent component analysis and the nonlinear matching pursuit method of the established overcomplete dictionary base, various fault mode components are separated. The initial phase function selected based on the high kurtosis fault frequency band obtained by the fast spectral kurtosis method can better fit the bearing fault frequency domain characteristics, so that the spatiotemporal intrinsic mode decomposition method can more accurately separate various impact components in the vibration signal. The simulation model of bearing compound fault was established and the data collected from fault diagnosis experiment platform were used to verify that the STIMD method was effective in solving the problem of rolling bearing compound fault diagnosis. By analyzing the kurtosis changes under different signal noise ratio (SNR) conditions and comparing the simulation results with the fast independent component analysis method, it shows that the kurtosis index decomposed by the proposed method is more able to prove the existence of faults under the condition of low SNR, that is, the impact is completely covered by noise. Therefore, a spatiotemporal intrinsic mode decomposition method with fast spectral kurtosis optimization can solve the problem of blind source separation in the field of composite faults of multi-channel rolling bearings and realize composite fault diagnosis.

Intelligent rolling bearing compound fault diagnosis based on frequency-domain Gramian angular field and convolutional neural networks with imbalanced data

The effective fault feature extraction is the core of rolling bearing fault diagnosis. However, rolling bearings usually operate in normal state and fault duration is very short, which will cause imbalance in fault diagnosis data, thus leading to difficulty in fault feature extraction and low diagnosis accuracy. Meanwhile, mutual interference between multiple fault responses will also lead to poor diagnosis performance. To solve these issues, a novel compound fault diagnosis method with imbalanced data based on frequency-domain Gramian angular field (FGAF) and convolutional neural networks optimized by instance normalization and efficient channel attention (IECNN) is proposed. Firstly, FGAF is adopted to map frequency-domain features of fault signals to the polar coordinate to obtain 2D FGAF feature spectrum. Secondly, an instance normalization module is established to reduce internal covariant shift caused by data distribution discrepancy and improve generalization ability. An efficient channel attention module is constructed to further excavate fault features and improve anti-interference ability. Finally, experiments are conducted under imbalanced dataset and imbalance intensified dataset, and the average accuracy of 99.91% and 99.92% were obtained, respectively, which shows the proposed method has better resistance to data imbalance.

A novel coupled array of multi-stable stochastic resonance under asymmetric trichotomous noise and its application in rolling bearing compound fault diagnosis

Rolling bearings are often subject to various types of damage in actual industrial operations. However, the early fault information is weak and completely overwhelmed by interference from noise, which is difficult to detect. In this paper, a novel coupled array of multi-stable stochastic resonance (NCAMSR) driven by asymmetric trichotomous noise is proposed for compound fault diagnosis. The Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) is used to separate the information of different fault features in the compound fault signal, and the outputs of the coupled array system are weighted to find the best effect. The spectral mean signal-to-noise ratio (SMSNR) is designed and the system parameters and weights are optimized using the particle swarm algorithm (PSO). To alleviate the problem of output saturation, the NCAMSR potential model based on the improved Gaussian potential is proposed, and the superior unsaturation of the NCAMSR potential function model is proven by theoretical analysis and experiments. Finally, the excellent performance of NCAMSR in compound fault detection is further verified by using engineering experiments with bearing data from Machinery Faults & Rotor Dynamics Simulator and real damage samples generated from accelerated life tests at Paderborn University, Germany.

Adaptive dynamic mode decomposition and its application in rolling bearing compound fault diagnosis

The decoupling detection of compound faults in rolling bearing is attracting considerable attentions. In recent years, some time-series decomposition methods, such as ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD), symplectic geometry mode decomposition (SGMD) etc., are used to extract the fault characteristics of bearing fault vibration signal and achieve the purpose of fault diagnosis. However, the fault characteristics of compound fault vibration signals are unevenly distributed, some fault characteristics are weakly disturbed by noise, which limit these methods application in compound fault diagnosis. In this paper, the Koopman operator is introduced from the perspective of flow field dynamics information extraction, an adaptive dynamic mode decomposition (ADMD) is proposed to decompose the nonlinear time-series data into a set of dynamic mode components (DMCs). In ADMD, a high-degree polynomial is applied to fit the data sequence of flow field, and a low dimensional subspace can be obtained. The dominant dynamics of flow field can be represented by the eigenvalues and eigenvectors of the low-dimensional subspace, which can be transformed into reconstructed time series with different frequency, and obtained the frequency-based DMCs. The simulation and experimental analysis results show that the proposed method can effectively decouple different fault components of compound fault of rolling bearing.

Rolling Bearing Compound Fault Diagnosis Based on Parameter Optimization MCKD and Convolutional Neural Network

For the sake of solving the problem of the difficulty of extracting fault features under the background of noise and accurately identify the state of the bearing, a compound fault diagnosis method of rolling bearing based on parameter optimization maximum correlated kurtosis deconvolution (MCKD) and convolutional neural network (CNN) is proposed. First, the adaptive multi-strategy cuckoo search algorithm (MSACS) is used to iteratively optimize the important parameters of MCKD. Second, the optimized MCKD is used to filter and denoise the rolling bearing fault signal, and the denoised signal is obtained. Finally, the denoised signal is input to the CNN model for training and testing to obtain the classification result of fault diagnosis. Through the test and evaluation of the fault dataset, the proposed method is compared with particle swarm optimization (PSO) parameter optimization method (PSO-MCKD-CNN) and CNN method without noise reduction. At the same time, it is compared with other advanced methods. The experimental results shows that this method improves the diagnostic performance of the neural network, obtains higher diagnostic accuracy, and is more conducive to the detection of compound faults.

A new method for rolling bearing compound fault diagnosis based on WT-PCA method

As it is difficult to extract the combined faults from rolling bearings of aero-engine in strong noise, a fault diagnosis method based on wavelet transform (WT), principal component analysis (PCA) and self-correlation noise reduction is proposed to solve this problem. The proposed method is then compared with the target matrix composed of maximum component of kurtosis, the largest and the second largest kurtosis value. The result of comparative analysis reveals that the 2D target matrix proposed in this paper works better to extract the characteristic frequency of combined faults of rolling bearings in terms of accuracy and effectiveness.

Sparsity-Oriented Nonconvex Nonseparable Regularization for Rolling Bearing Compound Fault under Noisy Environment

Rolling bearing is widely used in rotating machinery and, at the same time, it is easy to be damaged due to harsh operating environments and conditions. As a result, rolling bearing is critical to the safe operation of the machinery devices. Compound fault of rolling bearing is not a simple superimposition of multiple single faults, but the coupling of multiple fault features, making the vibration signal, becomes complicated. In our study, sparsity-oriented nonconvex nonseparable regularization (SONNR) method is proposed to rolling bearing compound fault diagnosis under noisy environment. Firstly, a theoretical model of rolling bearing compound fault is established, and the vibration characteristics of rolling bearing compound fault are analyzed. Secondly, four-layer structure of the SONNR method is proposed: input layer, nonconvex sparse regularization layer, signal reconstruction layer, and compound faults isolation layer. Finally, the validity of the method is verified by simulation data and actual data, and it is compared with the traditional time domain diagnostic methods and artificial intelligence methods.

Negentropy Spectrum Decomposition and Its Application in Compound Fault Diagnosis of Rolling Bearing.

The rolling bearings often suffer from compound fault in practice. Compared with single fault, compound fault contains multiple fault features that are coupled together and make it difficult to detect and extract all fault features by traditional methods such as Hilbert envelope demodulation, wavelet transform and empirical node decomposition (EMD). In order to realize the compound fault diagnosis of rolling bearings and improve the diagnostic accuracy, we developed negentropy spectrum decomposition (NSD), which is based on fast empirical wavelet transform (FEWT) and spectral negentropy, with cyclic extraction as the extraction method. The infogram is constructed by FEWT combined with spectral negentropy to select the best band center and bandwidth for band-pass filtering. The filtered signal is used as a new measured signal, and the fast empirical wavelet transform combined with spectral negentropy is used to filter the new measured signal again. This operation is repeated to achieve cyclic extraction, until the signal no longer contains obvious fault features. After obtaining the envelope of all extracted components, compound fault diagnosis of rolling bearings can be realized. The analysis of the simulation signal and the experimental signal shows that the method can realize the compound fault diagnosis of rolling bearings, which verifies the feasibility and effectiveness of the method. The method proposed in this paper can detect and extract all the fault features of compound fault completely, and it is more reliable for the diagnosis of compound fault. Therefore, the method has practical significance in rolling bearing compound fault diagnosis.

The FERgram: A rolling bearing compound fault diagnosis based on maximal overlap discrete wavelet packet transform and fault energy ratio

Compound fault features of the rolling bearing are difficult to separate and extract. To address this problem, the present paper proposed a diagnosis algorithm, namely FERgram, on the base of maximal overlap discrete wavelet packet transform (MODWPT) and fault energy ratio (FER). First, a group of frequency band signals are gained after MODWPT processing the initial vibration signal. Second, FER is chosen as the evaluation index, and then the FER values of each frequency band signal are calculated and used to generate FERgram. The frequency band signal with the maximum FER value containing plentiful fault information is chosen for envelope analysis. Finally, the fault type is determined by contrasting the prominent frequency component of the envelope spectrum with the fault feature frequency. The feasibility and superiority of the FERgram method are verified by four signals and four comparison methods. The results show that the FERgram method can effectively extract and accurately diagnose the compound fault of rolling bearing.