A hybrid classification autoencoder for semi-supervised fault diagnosis in rotating machinery

•A novel semi-supervised fault diagnosis method is proposed.•The model can be trained using both labeled and unlabeled data simultaneously.•The performance of the proposed method is experimentally validated on two kinds of facilities. Accurate fault diagnosis is critical to the safe and reliable ope...

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Veröffentlicht in:Mechanical systems and signal processing Jg. 149; S. 107327
Hauptverfasser: Wu, Xinya, Zhang, Yan, Cheng, Changming, Peng, Zhike
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin Elsevier Ltd 15.02.2021
Elsevier BV
Schlagworte:
Classification
Datasets
Fault diagnosis
Feature extraction
Hybrid classification autoencoder
Hydraulic turbines
Machine learning
Rotating machinery
Semi-supervised learning
Fault diagnosis
Rotating machinery
Semi-supervised learning
Hybrid classification autoencoder
ISSN:0888-3270, 1096-1216
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Zusammenfassung:•A novel semi-supervised fault diagnosis method is proposed.•The model can be trained using both labeled and unlabeled data simultaneously.•The performance of the proposed method is experimentally validated on two kinds of facilities. Accurate fault diagnosis is critical to the safe and reliable operation of rotating machinery. Intelligent fault diagnosis techniques based on deep learning have recently gained increasing attention due to their ability to rapidly and efficiently extract features from data and provide accurate diagnosis results. Most of the successes achieved by the state-of-the-art fault diagnosis methods are obtained through supervised learning, which requires a substantial set of labeled data. To reduce the dependence of the fault diagnosis method on labeled data and make full use of the more abundant unlabeled data, a semi-supervised fault diagnosis method called hybrid classification autoencoder is proposed in this paper. This newly designed model utilizes a softmax classifier to directly diagnose the health condition based on the encoded features from the autoencoder. The commonly used mean square error (MSE) of unsupervised autoencoder is also modified to adopt the labels of data, therefore the model can be trained using the labeled and unlabeled data simultaneously. The proposed method is validated by a motor bearing dataset and an industrial hydro turbine dataset. The results show that the proposed method can obtain fairly high diagnosis accuracies and surpass the existing methods on a very small fraction of labeled data.
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ISSN:0888-3270
1096-1216
DOI:10.1016/j.ymssp.2020.107327