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Multiple sensor data fusion is necessary for effective condition monitoring as the electric machines operate in a wide range of diverse operations. This study investigates sensor acquired vibration and current signals to establish a reliable multi-fault diagnosis framework of a brushless DC (BLDC) motor. Faults in stator and rotor were created deliberately by shorting two adjacent windings and creating a hole on the surface, respectively. The threshold for different health states was obtained by the third harmonic analysis of motor current. Later, the key features from sensor acquired current and vibration signals are selected based on monotonicity and reduced using the principal component analysis (PCA). For future predictions, an artificial neural network (ANN) is used to classify different fault features and its performance is evaluated using several metrics. Analysis of motor current harmonics and impulsive vibration response at the same time provides a thorough health estimation of BLDC motor in the presence of both electrical and mechanical faults. Multiple sensor information is fused to obtain a better understanding of the fault characteristics and mitigate the randomness of fault diagnosis. The proposed model was able to detect and classify multiple fault features with higher accuracy compared to other similar methods.

ANN Assisted Multi Sensor Information Fusion for BLDC Motor Fault Diagnosis