Composite Fault Diagnosis of Rolling Bearing Based on Optimized Wavelet Packet AR Spectrum Energy Entropy Combined with Adaptive No Velocity Term PSO-SOM-BPNN

Aiming at the problem of low diagnosis efficiency and accuracy, due to noise and cross aliasing among various faults when diagnosing composite faults of rolling bearing under actual working conditions, a composite fault diagnosis method of rolling bearing based on optimized wavelet packet autoregres...

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Bibliographic Details
Published inJournal of sensors Vol. 2021; no. 1
Main Authors Fan, Hongwei, Yan, Yang, Zhang, Xuhui, Cao, Xiangang, Ma, Jiateng
Format Journal Article
LanguageEnglish
Published New York Hindawi 2021
Hindawi Limited
Subjects
Accuracy
Algorithms
Artificial neural networks
Autoregressive processes
Back propagation networks
Classification
Decomposition
Efficiency
Energy
Entropy
Fault diagnosis
Feature extraction
Neural networks
Particle swarm optimization
Roller bearings
Self organizing maps
Velocity
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Summary:Aiming at the problem of low diagnosis efficiency and accuracy, due to noise and cross aliasing among various faults when diagnosing composite faults of rolling bearing under actual working conditions, a composite fault diagnosis method of rolling bearing based on optimized wavelet packet autoregressive (AR) spectral energy entropy and adaptive no velocity term particle swarm optimization-self organizing map-back propagation neural network (ANVTPSO-SOM-BPNN) is proposed. The energy entropy feature is extracted from the bearing vibration signal through wavelet packet AR spectrum, and SOM and BPNN are combined to form a series network. For PSO, the velocity term is discarded and the inertia weight and learning factor are adaptively adjusted. Finally, the Dempster-Shafer (D-S) evidence fusion diagnosis is carried out. To get closer to the application condition, the data are collected near and far away from the fault point for the composite fault diagnosis, which verifies the effectiveness of the proposed method.
ISSN:1687-725X
1687-7268
DOI:10.1155/2021/4138652