Abstract
Aiming at the inconsistent distribution of labeled and unlabeled data categories in the actual industrial production process, this paper proposes an open-set semi-supervised process fault diagnosis method based on uncertainty distribution alignment. Firstly, the proposed method forces the matching of the distribution of labeled data and unlabeled data. Then it combines a semi-supervised fault diagnosis model with the anomaly detection of one-vs-all classifier. The interior point (unlabeled samples in known class) is correctly classified while rejecting outliers to realize the fault diagnosis of open-set industrial process data. Finally, fault diagnosis experiments are carried out through numerical simulation and Tennessee-Eastman chemical process to verify the effectiveness and feasibility of the proposed method. Compared with temporal ensembling-dual student (TE-DS) and other semi-supervised fault diagnosis methods, it is proved that the proposed method is suitable for open-set fault diagnosis.
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Acknowledgements
This work was supported by the National Key R&D Program of China under Grant No. 2019YFB1706203.
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Liu, J., Song, H., Wang, J. (2022). Open-Set Fault Diagnosis Method for Industrial Process Based on Semi-supervised Learning. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13458. Springer, Cham. https://doi.org/10.1007/978-3-031-13841-6_10
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DOI: https://doi.org/10.1007/978-3-031-13841-6_10
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