Fault Diagnosis of Rotating Machinery Using Denoising-Integrated Sparse Autoencoder Based Health State Classification-Reference-Cited by-同舟云学术

Fault Diagnosis of Rotating Machinery Using Denoising-Integrated Sparse Autoencoder Based Health State Classification

Published:2023 Issue: Volume:11 Page:15174-15183
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Yang Jing¹^ORCID,Xie Guo²,Yang Yanxi²

Affiliation:

1. School of Mechatronics and Automotive Engineering, Tianshui Normal University, Tianshui, China

2. School of Automation and Information Engineering, Xi’an University of Technology, Xi’an, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Gansu Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10044096.pdf?arnumber=10044096

Reference35 articles.

1. Deep learning for fault diagnosis: The state of the art and challenge;ren;Control Decis,2017

2. Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: A benchmark data set for data-driven classification;lessmeier;Proc Eur Conf Prognostics Health Manage Soc,2016

3. Health Degradation Monitoring and Early Fault Diagnosis of a Rolling Bearing Based on CEEMDAN and Improved MMSE

4. An SVM—ANN Hybrid Classifier for Diagnosis of Gear Fault

5. Diagnosis of Incipient Fault Based on Sliding-Scale Resampling Strategy and Improved Deep Autoencoder

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