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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Vydané v:	IEEE transactions on industrial informatics Ročník 21; číslo 2; s. 1329 - 1338
Hlavní autori:	Zhao, Yinghao, Yang, Xu, Huang, Jian, Gao, Jingjing, Zhou, Xian, Zhang, Tao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.02.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
On-line prístup:	Získať plný text
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