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

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. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

Accurate fault diagnosis is crucial for the safe operation of rotating machinery. The proposed method, hybrid classification autoencoder, utilizes both labeled and unlabeled data for training and achieved high diagnostic accuracies in experiments with minimal labeled data. This approach shows potential for more efficient fault diagnosis in the future.