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T 2 -LSTM-Based AI System for Early Detection of Motor Failure in Chemical Plants

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  • Chien-Chih Wang

    (Department of Industrial Engineering and Management, Ming Chi University of Technology, New Taipei City 24303, Taiwan)

Abstract

In the chemical industry, stable reactor operation is essential for consistent production. Motor failures can disrupt operations, resulting in economic losses and safety risks. Traditional monitoring methods, based on human experience and simple current monitoring, often need to be faster and more accurate. The rapid development of artificial intelligence provides powerful tools for early fault detection and maintenance. In this study, the Hotelling T 2 index is used to calculate the root mean square values of the normal motor’s x, y, and z axes. A long short-term memory (LSTM) model creates a trend model for the Hotelling T 2 index, determining an early warning threshold. Current anomaly detection follows the ISO 10816-1 standard, while future anomaly prediction uses the T 2 -LSTM trend model. Validated at a chemical plant in Southern Taiwan, the method shows 98% agreement between the predicted and actual anomalies over three months, demonstrating its effectiveness. The T 2 -LSTM model significantly improves the accuracy of motor fault detection, potentially reducing economic losses and improving safety in the chemical industry. Future research will focus on reducing false alarms and integrating more sensor data.

Suggested Citation

  • Chien-Chih Wang, 2024. "T 2 -LSTM-Based AI System for Early Detection of Motor Failure in Chemical Plants," Mathematics, MDPI, vol. 12(17), pages 1-15, August.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:17:p:2652-:d:1464599
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    References listed on IDEAS

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