Expert system for induction motor fault detection based on vibration analysis

Abstract

This paper presents an expert system for induction motor fault detection based on vibration analysis and support vector machines (SVM). Vibration signals of healthy and faulty induction motors are collected and characteristic features, as indicator of fault presence, are calculated, in both time and frequency domain. Two types of faults were considered, static eccentricity and bearing wear. Obtained feature sets were then used for training of support vector machines classifiers, a type of artificial intelligence classification technique which determines whether some of considered faults is present or not. An expert system for fault detection is designed combining a database of calculated features and trained SVM classifiers. This system was tested and validated on a number of healthy and faulty motors in the laboratory and in industrial facility for sunflower oil processing. Obtained results prove that this system can detect faults in early stages with high accuracy and reliability. Thus, it provides malfunction and failure prevention and improves overall performance and efficiency of industrial systems.

Keywords

• fault detection
• induction motor
• vibration analysis
• support vector machines