Fast Adaption for Multi Motor Anomaly Detection via Meta Learning and deep unsupervised learning

Yi Cheng Yu, Shang Wen Chuang, Hong Han Shuai, Chen-Yi Lee

研究成果: Conference contribution同行評審

摘要

For PHM(Predictive Health Management) in an industrial scenario, motor vibration anomaly detection requires a large number of labeled data. The problem is that the anomaly vibration signal data is scarce and difficult to be obtained, especially the problem of insufficient data in the building and deployment of multi-machine models. This paper proposes a Meta-learning method based on deep unsupervised learning(Autoencoder). We use non-labeled and a small amount of vibration signal data which is converted into 52 key physical and statistical features to build models of different motors. By inputting these 52 features into designed models, other 52 features can be reconstructed as output. We use input and output features to calculate reconstruction error which is used as the criterion for the performance of models. We also use accuracy as the criterion for the performance of models. In the actual industrial situation, the improvement of accuracy by our proposed method is about 33.50% better than the method only with unsupervised learning for the new sensor model with few vibration data.

原文English
主出版物標題2022 IEEE 31st International Symposium on Industrial Electronics, ISIE 2022
發行者Institute of Electrical and Electronics Engineers Inc.
頁面1186-1189
頁數4
ISBN(電子)9781665482400
DOIs
出版狀態Published - 2022
事件31st IEEE International Symposium on Industrial Electronics, ISIE 2022 - Anchorage, United States
持續時間: 1 6月 20223 6月 2022

出版系列

名字IEEE International Symposium on Industrial Electronics
2022-June

Conference

Conference31st IEEE International Symposium on Industrial Electronics, ISIE 2022
國家/地區United States
城市Anchorage
期間1/06/223/06/22

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