An Effective Stator Fault Diagnosis Framework of BLDC Motor Based on Vibration and Current Signals

Abstract

Electric motor is a prominent rotary machinery in many engineering applications due to its excellent electrical energy utilization. With the increased demand in production and complex operating conditions, motors often run in a severe loading condition. Overload, overheating and many other intricate operating conditions account for the stator related faults in motors. Motor current signature analysis (MCSA) and vibration analysis have been popular techniques to diagnose different stator and rotor related faults in motors. But it is difficult to find the fault magnitude or fault threshold by using only one approach due to nonstationary motor operations. This paper presents a comprehensive review of a permanent magnet brushless DC motor’s (BLDC motor) fault diagnosis combining vibration and current signals collected from sensors. Since the insulation break in the stator winding is the most commonly occurring fault in the stator, a short-circuit was artificially created between two windings. Based on the motor operating conditions, three health states are chosen from the experimental sensor data with different fault magnitudes. Health states are labeled as healthy state, incipient failure state, and severe failure state. Two effective fault diagnosis indices named kurtosis and third harmonic of motor current are selected for analyzing the vibration signals and current signals, respectively. Proposed diagnostics framework is validated using experimental data and proven to detect the stator fault at the early stage as well as distinguish between different fault states. Monitoring both mechanical and electrical characteristics of BLDC motor provides a thorough understanding of fault magnitude and threshold in different health states.