A Review on Condition Monitoring and Diagnostic Techniques of Rotating Electrical Machines

Electrical machines are critical components in industrial processes. A motor failure may yield an unexpected interruption at the industrial plant, with consequences in costs, product quality, and safety. To determine the conditions of each part of motor, various testing and monitoring methods have been developed. In this paper, a review on effective fault indicators and condition monitoring methods of rotating electrical machines has been accomplished. Fault detection methods divided to four groups: electrical, mechanical, chemical and thermal indicators. Some fault detection methods based on electrical symptoms like stator current, voltage, their combination or spectrum discussed in electrical group. In second branch, mechanical symptoms like torque, vibration and so on used for condition monitoring. Third group, chemical indicators, assigned to some chemical parameters of materials like oil characteristic or wear and debris in oil analysis. In last group, thermal symptoms in rotating electrical machines will be spoken. Between all methods, some of them are more known like vibration and some of them are recently added like motor current signature analysis (MCSA). Nowadays, combined methods and methods used artificial intelligence (AI) in condition monitoring are more popular. In every group, the fault detection method and the faults that can be detected have been mentioned. Mathematical equations of some new signal processing method have been discussed in literature presented in appendix.