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

Chien-Chih Wang ()
Additional contact information
Chien-Chih Wang: Department of Industrial Engineering and Management, Ming Chi University of Technology, New Taipei City 24303, Taiwan

Mathematics, 2024, vol. 12, issue 17, 1-15

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.

Keywords: predictive maintenance; T 2 -LSTM framework; motor fault detection; artificial intelligence; sustainable chemical industry (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/17/2652/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/17/2652/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:17:p:2652-:d:1464599

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().