A reliable unsupervised sensor data fusion method for fault detection in brushless direct current motors

Abstract

This paper introduces an efficient and reliable unsupervised method for detecting faults in a brushless direct current (BLDC) motor based on abnormality identification in sensor-acquired vibration and sound signals through multi resolution decompostion and analysis. The research utilizes the double-density dual-tree complex wavelet transform (DD-DT-CWT) to extract important features from vibration signals, and incorporates audio feature extraction for the sound signals. The captured signals are divided into overlapping segments to improve fault localization, and the features of each segment are organized in a coefficient matrix. Subsequently, singular value decomposition (SVD) is applied to the resulting coefficient matrix from the vibration and audio signals. To effectively monitor the motor’s condition, the singular values from both sets of sensor data are combined. Analysing the decay patterns of the singular values enables the identification of faults in the BLDC motor under test. By establishing a suitable threshold for the decay slope of the singular values, the proposed method can accurately and precisely identify and categorize various faults in BLDC motors. This early fault detection can prompt predictive maintenance to ensure the optimal performance, reduced downtime and longevity of BLDC motors.