Fault Identi fication of Rolling Bearing Based on Adaptive Wavelet Analysis and Multiple Layers Convolution Extreme Learning Auto-encoder

Aiming at the problems of rolling bearing vibration signals were dif ficult to identify due to strong time-varying and strong noisy characteristics, a method based on adaptive wavelet analysis (AWA) and multiple layers convolution extreme learning auto-encoder (MLCELAE) was proposed. Firstly, a new...

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Bibliographic Details

Published in:	机车电传动
Main Author:	Yahong TAN
Format:	Article
Language:	Chinese
Published:	Editorial Department of Electric Drive for Locomotives 2021-11-01
Subjects:	bearing adaptive wavelet analysis fault identi fication convolution extreme learning auto-encoder
Online Access:	http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128x.2021.06.015