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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Autores principales:	Hongwei Fan, Yang Yan, Xuhui Zhang, Xiangang Cao, Jiateng Ma
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Technology (General) T1-995
Acceso en línea:	https://doaj.org/article/9d34759fdc9d48ac9a8177d26fa85dcb
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spelling	oai:doaj.org-article:9d34759fdc9d48ac9a8177d26fa85dcb2021-11-29T00:56:36ZComposite Fault Diagnosis of Rolling Bearing Based on Optimized Wavelet Packet AR Spectrum Energy Entropy Combined with Adaptive No Velocity Term PSO-SOM-BPNN1687-726810.1155/2021/4138652https://doaj.org/article/9d34759fdc9d48ac9a8177d26fa85dcb2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/4138652https://doaj.org/toc/1687-7268Aiming 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.Hongwei FanYang YanXuhui ZhangXiangang CaoJiateng MaHindawi LimitedarticleTechnology (General)T1-995ENJournal of Sensors, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology (General) T1-995
spellingShingle	Technology (General) T1-995 Hongwei Fan Yang Yan Xuhui Zhang Xiangang Cao Jiateng Ma Composite Fault Diagnosis of Rolling Bearing Based on Optimized Wavelet Packet AR Spectrum Energy Entropy Combined with Adaptive No Velocity Term PSO-SOM-BPNN
description	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.
format	article
author	Hongwei Fan Yang Yan Xuhui Zhang Xiangang Cao Jiateng Ma
author_facet	Hongwei Fan Yang Yan Xuhui Zhang Xiangang Cao Jiateng Ma
author_sort	Hongwei Fan
title	Composite Fault Diagnosis of Rolling Bearing Based on Optimized Wavelet Packet AR Spectrum Energy Entropy Combined with Adaptive No Velocity Term PSO-SOM-BPNN
title_short	Composite Fault Diagnosis of Rolling Bearing Based on Optimized Wavelet Packet AR Spectrum Energy Entropy Combined with Adaptive No Velocity Term PSO-SOM-BPNN
title_full	Composite Fault Diagnosis of Rolling Bearing Based on Optimized Wavelet Packet AR Spectrum Energy Entropy Combined with Adaptive No Velocity Term PSO-SOM-BPNN
title_fullStr	Composite Fault Diagnosis of Rolling Bearing Based on Optimized Wavelet Packet AR Spectrum Energy Entropy Combined with Adaptive No Velocity Term PSO-SOM-BPNN
title_full_unstemmed	Composite Fault Diagnosis of Rolling Bearing Based on Optimized Wavelet Packet AR Spectrum Energy Entropy Combined with Adaptive No Velocity Term PSO-SOM-BPNN
title_sort	composite fault diagnosis of rolling bearing based on optimized wavelet packet ar spectrum energy entropy combined with adaptive no velocity term pso-som-bpnn
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/9d34759fdc9d48ac9a8177d26fa85dcb
work_keys_str_mv	AT hongweifan compositefaultdiagnosisofrollingbearingbasedonoptimizedwaveletpacketarspectrumenergyentropycombinedwithadaptivenovelocitytermpsosombpnn AT yangyan compositefaultdiagnosisofrollingbearingbasedonoptimizedwaveletpacketarspectrumenergyentropycombinedwithadaptivenovelocitytermpsosombpnn AT xuhuizhang compositefaultdiagnosisofrollingbearingbasedonoptimizedwaveletpacketarspectrumenergyentropycombinedwithadaptivenovelocitytermpsosombpnn AT xiangangcao compositefaultdiagnosisofrollingbearingbasedonoptimizedwaveletpacketarspectrumenergyentropycombinedwithadaptivenovelocitytermpsosombpnn AT jiatengma compositefaultdiagnosisofrollingbearingbasedonoptimizedwaveletpacketarspectrumenergyentropycombinedwithadaptivenovelocitytermpsosombpnn
_version_	1718407722936827904

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

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