Real-time Vibration Signals Anomaly Detection of Load Motor Based on Deep Learning and One-Class Classification

Tang Chia-Chien, Hung Kai Wang

研究成果: Conference contribution

摘要

The objective of this research is to build a robust fault detection system that is applicable to load motor for various working conditions. What’s more, during the model training stage, only vibration signals under normal conditions are used, as it can be difficult to obtain abnormal vibration signals in practical scenarios in the manufacturing industry. In this paper, the aging phenomenon of the load motor is simulated by adjusting stiffness. The research methodology is comprised of two stages: vibration signal prediction and anomaly detection. Three different networks were used for vibration prediction: Recurrent Neural Network (RNN), Gate Recurrent Unit (GRU) a combination of One-Dimensional Convolutional Neural Network (1DCNN) and GRU. Calculating statistical features, such as Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), to assess predicted results and served as input features for anomaly detection. In the stage of anomaly detection, The Support Vector Data Description (SVDD) is a method used to determine a damage threshold, indicating that machines exceeding this threshold are considered damaged.

原文English
主出版物標題28th ISSAT International Conference on Reliability and Quality in Design, RQD 2023
發行者International Society of Science and Applied Technologies
頁面200-204
頁數5
ISBN(電子)9798986576121
出版狀態Published - 2023
事件28th ISSAT International Conference on Reliability and Quality in Design, RQD 2023 - San Francisco, United States
持續時間: 2023 8月 32023 8月 5

出版系列

名字28th ISSAT International Conference on Reliability and Quality in Design, RQD 2023

Conference

Conference28th ISSAT International Conference on Reliability and Quality in Design, RQD 2023
國家/地區United States
城市San Francisco
期間23-08-0323-08-05

All Science Journal Classification (ASJC) codes

  • 安全、風險、可靠性和品質

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