An Intelligent Multi-Agent System Framework for Fault Diagnosis of Squirrel-Cage Induction Motor Broken Bars

Decision making in fault diagnosis is a critical factor in industrial production maintenance. Artificial intelligence (AI) techniques are widely used to accurately identify faults in induction motors. Multi-agent systems (MAS) as a distributed AI method are efficient in representing intelligent manufacturing systems to achieve decision robustness. In this paper, an intelligent MAS is developed for the decision making in fault diagnosis of three-phase squirrel cage induction motor rotor bars. Agents in the proposed MAS represent induction motor in different health conditions, i.e. healthy motor and motor with 1, 2 and 3 broken bars and also a supervisor agent. Each agent is embedded with an artificial neural network and trained with measurement data taken from a motor in the corresponding health condition. Measurement data are obtained with the classical motor current signature analysis (MSCA) method. Each agent makes local decision making and communicates its output to the supervisor agent that makes the final fault diagnosis based on a threshold value.

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