scholarly journals A Rolling Bearing Fault Classification Scheme Based on k-Optimized Adaptive Local Iterative Filtering and Improved Multiscale Permutation Entropy

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 191
Author(s):  
Yi Zhang ◽  
Yong Lv ◽  
Mao Ge
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Bp Neural Network ◽  
Rolling Bearing ◽  
Fault Classification ◽  
Permutation Entropy ◽  
Fault Identification ◽  
Depth Information ◽  
Bearing Fault ◽  
Iterative Filtering

The health condition of the rolling bearing seriously affects the operation of the whole mechanical system. When the rolling bearing parts fail, the time series collected in the field generally shows strong nonlinearity and non-stationarity. To obtain the faulty characteristics of mechanical equipment accurately, a rolling bearing fault detection technique based on k-optimized adaptive local iterative filtering (ALIF), improved multiscale permutation entropy (improved MPE), and BP neural network was proposed. In the ALIF algorithm, a k-optimized ALIF method based on permutation entropy (PE) is presented to select the number of ALIF decomposition layers adaptively. The completely average coarse-graining method was proposed to excavate more hidden information. The performance analysis of the simulation signal shows that the improved MPE can more accurately dig out the depth information of the time series, and the entropy value obtained is more consistent and stable. In the research application, rolling bearing time series are decomposed by k-optimized ALIF to obtain a certain number of intrinsic mode functions (IMFs). Then the improved MPE value of effective IMF is calculated and input into backpropagation (BP) neural network as the feature vector for automatic fault identification. The comparative analysis of simulation signals shows that this method can extract fault information effectively. At the same time, the experimental part shows that this scheme not only effectively extracts the fault features, but also realizes the classification and identification of different fault modes and faults of different degrees, which has a certain application prospect in the research and application direction of rolling bearing fault identification.

scholarly journals Rolling Bearing Fault Prediction Method Based on QPSO-BP Neural Network and Dempster–Shafer Evidence Theory

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1094 ◽  
Author(s):  
Lanjun Wan ◽  
Hongyang Li ◽  
Yiwei Chen ◽  
Changyun Li
Keyword(s):  
Neural Network ◽  
Working Conditions ◽  
Bp Neural Network ◽  
Evidence Theory ◽  
Prediction Method ◽  
Rolling Bearing ◽  
Fault Prediction ◽  
Fault Classification ◽  
Bearing Fault ◽  
Hidden Layer

To effectively predict the rolling bearing fault under different working conditions, a rolling bearing fault prediction method based on quantum particle swarm optimization (QPSO) backpropagation (BP) neural network and Dempster–Shafer evidence theory is proposed. First, the original vibration signals of rolling bearing are decomposed by three-layer wavelet packet, and the eigenvectors of different states of rolling bearing are constructed as input data of BP neural network. Second, the optimal number of hidden-layer nodes of BP neural network is automatically found by the dichotomy method to improve the efficiency of selecting the number of hidden-layer nodes. Third, the initial weights and thresholds of BP neural network are optimized by QPSO algorithm, which can improve the convergence speed and classification accuracy of BP neural network. Finally, the fault classification results of multiple QPSO-BP neural networks are fused by Dempster–Shafer evidence theory, and the final rolling bearing fault prediction model is obtained. The experiments demonstrate that different types of rolling bearing fault can be effectively and efficiently predicted under various working conditions.

Rolling bearing fault diagnosis of PSO–LSSVM based on CEEMD entropy fusion

2020 ◽  
Vol 44 (3) ◽  
pp. 405-418
Author(s):  
Shuzhi Gao ◽  
Tianchi Li ◽  
Yimin Zhang
Keyword(s):  
Fault Diagnosis ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Fault Classification ◽  
Kernel Principal Component Analysis ◽  
Permutation Entropy ◽  
Support Vector ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Taking aim at the nonstationary nonlinearity of the rolling bearing vibration signal, a rolling bearing fault diagnosis method based on the entropy fusion feature of complementary ensemble empirical mode decomposition (CEEMD) is proposed in combination with information fusion theory. First, CEEMD of the vibration signal of the rolling bearing is performed. Then the signal is decomposed into the sum of several intrinsic mode functions (IMFs), and the singular entropy, energy entropy, and permutation entropy are obtained for the IMFs with fault features. Second, the feature extraction method of entropy fusion is proposed, and the three entropy data obtained are input into kernel principal component analysis (KPCA) for feature fusion and dimensionality reduction to obtain complementary features. Finally, the extracted features are imported into the particle swarm optimization (PSO) algorithm to optimize the least-squares support vector machine (LSSVM) for fault classification. Through experimental verification, the proposed method can be used for roller bearing fault diagnosis.

scholarly journals Rolling Bearing Fault Diagnosis Based on Wavelet Packet Transform and Convolutional Neural Network

2020 ◽  
Vol 10 (3) ◽  
pp. 770 ◽  
Author(s):  
Guoqiang Li ◽  
Chao Deng ◽  
Jun Wu ◽  
Zuoyi Chen ◽  
Xuebing Xu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Wavelet Packet Transform ◽  
Fault Classification ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Timely sensing the abnormal condition of the bearings plays a crucial role in ensuring the normal and safe operation of the rotating machine. Most traditional bearing fault diagnosis methods are developed from machine learning, which might rely on the manual design features and prior knowledge of the faults. In this paper, based on the advantages of CNN model, a two-step fault diagnosis method developed from wavelet packet transform (WPT) and convolutional neural network (CNN) is proposed for fault diagnosis of bearings without any manual work. In the first step, the WPT is designed to obtain the wavelet packet coefficients from raw signals, which then are converted into the gray scale images by a designed data-to-image conversion method. In the second step, a CNN model is built to automatically extract the representative features from gray images and implement the fault classification. The performance of the proposed method is evaluated by a real rolling-bearing dataset. From the experimental study, it can be seen the proposed method presents a more superior fault diagnosis capability than other machine-learning-based methods.

Fault Diagnosis of Railway Axlebox Bearing Based on Wavelet Packet and Neural Network

2012 ◽  
Vol 226-228 ◽  
pp. 749-755 ◽  
Author(s):  
Xiao Feng Li ◽  
Yun Xiao Fu ◽  
Li Min Jia
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Wavelet Packet ◽  
Diagnostic System ◽  
Vibration Signal ◽  
Fault Classification ◽  
Wavelet Packet Decomposition ◽  
Bearing Fault ◽  
Fault Diagnostic

A real time and effective axlebox bearing fault diagnostic method is significant in the condition-based maintenance. In the axlebox bearing fault diagnostic system, fault features extraction and fault patterns classification are two important aspects to identify whether a axlebox bearing is failure or not. This paper presents a method of axlebox bearing fault diagnosis based on wavelet packet decomposition and BP neural network. First decompose the vibration signal into a finite number of coefficients by wavelet packet decomposition. Then calculate energy moment of each coefficient and take the energy moment as an eigenvector to effectively express the failure feature. Finally BP neural network is used for fault classification. The experimental results show that combining wavelet packet decomposition with BP neural network could identify the axlebox bearing fault effectively. The average diagnosis accuracy rate is 96.67%.

scholarly journals Time-Shift Multi-scale Weighted Permutation Entropy and GWO-SVM Based Fault Diagnosis Approach for Rolling Bearing

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 621 ◽  
Author(s):  
Zhilin Dong ◽  
Jinde Zheng ◽  
Siqi Huang ◽  
Haiyang Pan ◽  
Qingyun Liu
Keyword(s):  
Time Series ◽  
Fault Diagnosis ◽  
Recognition Rate ◽  
Rolling Bearing ◽  
Coarse Grained ◽  
Fault Classification ◽  
Time Shift ◽  
Permutation Entropy ◽  
Support Vector ◽  
Multi Scale

Multi-scale permutation entropy (MPE) is an effective nonlinear dynamic approach for complexity measurement of time series and it has been widely applied to fault feature representation of rolling bearing. However, the coarse-grained time series in MPE becomes shorter and shorter with the increase of the scale factor, which causes an imprecise estimation of permutation entropy. In addition, the different amplitudes of the same patterns are not considered by the permutation entropy used in MPE. To solve these issues, the time-shift multi-scale weighted permutation entropy (TSMWPE) approach is proposed in this paper. The inadequate process of coarse-grained time series in MPE was optimized by using a time shift time series and the process of probability calculation that cannot fully consider the symbol mode is solved by introducing a weighting operation. The parameter selections of TSMWPE were studied by analyzing two different noise signals. The stability and robustness were also studied by comparing TSMWPE with TSMPE and MPE. Based on the advantages of TSMWPE, an intelligent fault diagnosis method for rolling bearing is proposed by combining it with gray wolf optimized support vector machine for fault classification. The proposed fault diagnostic method was applied to two cases of experimental data analysis of rolling bearing and the results show that it can diagnose the fault category and severity of rolling bearing accurately and the corresponding recognition rate is higher than the rate provided by the existing comparison methods.

Rolling Bearing Fault Diagnosis Method Using Glowworm Swarm Optimization and Artificial Neural Network

2013 ◽  
Vol 860-863 ◽  
pp. 1812-1815 ◽  
Author(s):  
Qiang Xu ◽  
Yong Qian Liu ◽  
De Tian ◽  
Quan Long
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Rolling Bearing ◽  
Maintenance Cost ◽  
Swarm Optimization ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Glowworm Swarm Optimization ◽  
Potential Applications

Fault diagnosis has long been recognised as one of the most effective methods of reducing operation and maintenance cost in rotating industry, especially in bearings. A method based on BP neural network modified by glowworm swarm optimization (GSO) was proposed for fault diagnosis of rolling bearings. Six fault features were selected as the input of network. GSO algorithm was applied to simultaneously optimize the initial weight and threshold values of BP neural network. The reliability of the proposed technique was confirmed by experimental data, which indicated the potential applications of this method in the field of rolling bearing fault diagnosis.

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