Gearbox Status Recognition based on TMD-SVD and POS-BP Networks Under Strong Interference

Gearbox Status Recognition based on TMD-SVD and POS-BP Networks Under Strong Interference

The vehicle gearbox has a bad working environment and the fault mode is difficult to identify. On the basis of existing methods， a method based on two-layer-mode decomposition （TMD） and singular value decomposition （SVD） is proposed， combined with particle swarm （POS）-BP neural network for fault dia...

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Bibliographic Details
Main Authors: Lei He, Suqi Liu
Format: Article
Language:zho
Published: Editorial Office of Journal of Mechanical Transmission 2021-05-01
Series:Jixie chuandong
Subjects:
Two-layer-mode decomposition（TMD）
Singular value decomposition（SVD）
POS-BP neural network
Fault diagnosis
Online Access:http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2021.05.025
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Summary:The vehicle gearbox has a bad working environment and the fault mode is difficult to identify. On the basis of existing methods， a method based on two-layer-mode decomposition （TMD） and singular value decomposition （SVD） is proposed， combined with particle swarm （POS）-BP neural network for fault diagnosis. Firstly， the vibration signals under four typical conditions of normal transmission， rolling failure， outer ring crack and gear wear are collected on a self-built experimental platform. Then， the first 5 IMFs components of the signal is decomposed by EMD， since the spectrum of IMF1 is still complicated， the wavelet packet is used to continue the 2-layer decomposition. Finally， the eight sub-sequences are obtained by TMD， and the signal component matrix is constructed. Then， the singular value （SVD） of the component matrix is extracted as the eigenvalue， the eigenvalues are entered into the constructed POS-BP neural network diagnostic model， and the gearbox fault type is identified based on the output. The analysis results show that the method can be effectively applied to the fault diagnosis of special vehicle gearboxes， and the diagnostic accuracy rate reaches 92%， which provides an effective reference for gearbox state recognition under complex conditions.
ISSN:1004-2539

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