A Novel Intelligent Fault Diagnosis Method for Rolling Bearings Based on Compressed Sensing and Stacked Multi-Granularity Convolution Denoising Auto-Encoder

A Novel Intelligent Fault Diagnosis Method for Rolling Bearings Based on Compressed Sensing and Stacked Multi-Granularity Convolution Denoising Auto-Encoder

This paper investigates the unsupervised automatic feature extraction method with a large amount of unlabeled data for the fault diagnosis of rolling bearings in automobile production line, where the fault information is hard to identify due to the low-level features of a single category and the mas...

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Bibliographic Details
Main Authors: Chuang Liang, Changzheng Chen, Ye Liu, Xinying Jia
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Bearing fault
intelligent diagnosis
feature extraction
compressive sensing
stacked auto-encoder
Online Access:https://ieeexplore.ieee.org/document/9618940/

Internet

https://ieeexplore.ieee.org/document/9618940/

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