scholarly journals Rolling Bearing Fault Monitoring Research Based on the BP Neural Network

2014 ◽  
Vol 03 (02) ◽  
pp. 57-66
Author(s):  
术娟 廖
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Rolling Bearing ◽  
Bearing Fault ◽  
Fault Monitoring

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 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.

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.

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