A novel multi-agent approach to identify faults in line connected three-phase induction motors

• Present a novel multi-agent approach to identify stator, rotor and bearing faults in three-phase induction motors.
• Proposed methodology uses the current amplitudes signal in time domain as the inputs of the multi-agent system for fault diagnosis.
• The multi-agent system incorporates pattern recognition techniques with better results for each type of fault.
• Experimental results gathered from three-phase induction motors operating with different load conditions and fed under unbalance voltage are provided.

Three-phase induction motors (TIMs) are the key elements of electromechanical energy conversion in a variety of productive sectors. Identifying a defect in a running motor, before a failure occurs, can provide greater security in the decision-making processes for machine maintenance, reduced costs and increased machine operation availability. This paper proposes a new approach for identifying faults and improving performance in three-phase induction motors by means of a multi-agent system (MAS) with distinct behavior classifiers. The faults observed are related to faulty bearings, breakages in squirrel-cage rotor bars, and short-circuits between the coils of the stator winding. By analyzing the amplitudes of the current signals in the time domain, experimental results are obtained through the different methods of pattern classification under various sinusoidal power and mechanical load conditions for TIMs. The use of an MAS to classify induction motor faults allows the agents to work in conjunction in order to perform a specific set of tasks and achieve the goals. This technique proved its effectiveness in the evaluated situations with 1 and 2 hp motors, providing an alternative tool to traditional methods to identify bearing faults, broken rotor bars and stator short-circuit faults in TIMs.

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