Intelligent Vibration-based Anomaly Detection for Electric Motor Condition Monitoring
Publish place: 9th Iranian Joint Congress on Fuzzy and Intelligent Systems
Publish Year: 1400
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
FJCFIS09_068
تاریخ نمایه سازی: 10 اردیبهشت 1401
Abstract:
The health state of rotating machinery can be represented by its vibration signal. With the help of machine learning approaches, the condition of rotating machinery such as electric motors can be diagnosed accurately. Thus, equipment down-time can be minimized and catastrophic accidents can be avoided. This paper presents an intelligent anomaly detection method for electric motors based on vibration signals. Due to lack of damage conditions information, only the normal condition data were employed to generate an unsupervised learning model for two different types of motor within the same class, which are the new laboratory motor and old industrial ones. The performance of the model to detect anomalies for both motors was studied, extensively. The results show that the model generated possesses the highest capability to detect anomalies for both motors as the normalized and mapped features were used.
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Authors
Tuan A. Z. Rahman
IoT Systems Laboratory, MIMOS Berhad ۵۷۰۰۰ Kuala Lumpur, Malaysia
Leong Wen Chek
IoT Systems Laboratory, MIMOS Berhad ۵۷۰۰۰ Kuala Lumpur, Malaysia
Nordin Ramli
IoT Systems Laboratory, MIMOS Berhad ۵۷۰۰۰ Kuala Lumpur, Malaysia