Ensemble Targeted Stacked Denoising Autoencoders With Mutual Information Constraint for Rotating Machinery Fault Diagnosis

Since the poor quality of signals and redundant features may reduce the accuracy of fault diagnosis of rotating machinery components, an ensemble targeted stacked denoising autoencoders (ETSDAE) method is proposed for machinery running under harsh environment. At first, a targeted denoising strategy...

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Bibliographic Details
Published in:	IEEE transactions on industrial informatics Vol. 21; no. 2; pp. 1329 - 1338
Main Authors:	Zhao, Yinghao, Yang, Xu, Huang, Jian, Gao, Jingjing, Zhou, Xian, Zhang, Tao
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.02.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Artificial neural networks Constraints Cost function Data models Deep features Fault diagnosis Feature extraction Frequency-domain analysis Machine learning Machinery Mutual information Noise reduction Redundancy Roller bearings Rotating machinery Signal quality stacked denoising autoencoder (SDAE) Time domain analysis Training
ISSN:	1551-3203, 1941-0050
Online Access:	Get full text
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