Study on fault diagnosis of broken rotor bars in squirrel cage induction motors: a multi‐agent system approach using intelligent classifiers

A critical factor in industrial production maintenance is decision-making in fault diagnosis. Motor current signature analysis (MCSA) is an established condition-based maintenance method to make fault diagnosis in induction motors (IMs). The occurrence of multiple interacting faults, as well as emergent behaviour, can be tackled efficiently especially when MCSA is combined with distributed problem-solving artificial intelligence (AI) techniques. Therefore, in this study, an intelligent multi-agent system (MAS) is presented to make decisions on the fault conditioning of a three-phase squirrel cage IM. The incorporated agents represent different health conditions of the same IM, with faults that may occur in the rotor bars. All agents utilise an AI method, and use for training MCSA experimental data. A supervisor agent (SA) initially communicates with agents employing feed-forward artificial neural networks trained with the back-propagation algorithm and performs the final fault diagnosis by evaluating their responses. When a decision cannot be made on the fault type, the SA employs another agent that uses the k-nearest neighbour rule. The proposed method achieves high fault diagnosis accuracy. Its performance is also compared with results previously obtained from the same motor when an adaptive neuro-fuzzy inference system combined with a subtractive clustering method had been used.