Fault diagnosis of brushless DC motor based on Stack Sparse Autoencoder

Abstract

Because of their simple structure, long service life, high efficiency, etc., brushless DC (BLDC) motors have been widely applied in many fields. In some applications, high requirements for BLDC continuous use of motors, so often to BLDC running state monitoring of motors, realizes the early fault diagnosis, to improve the reliability, safety, and prolonged use. To solve this problem, a BLDC fault diagnosis method based on Fast Fourier Transform (FFT), Stacked Sparse Auto-encoder (SSAE), and soft classifier was proposed. In the laboratory, a BLDC model R-3525 was used as the experimental equipment, and the data of 9 kinds of fault conditions such as bearing damage, cage damage, inner and outer ring damage, and the normal condition of the motor were collected. The collected data are transformed into 28 × 28 two-dimensional data sets by FFT. The feature expressions of various faults are adaptively learned from many data, and the intelligent diagnosis of the motor is realized by the feature function expression. The experimental results demonstrate that, in comparison to the single neural network approach, the stacked method can significantly enhance the precision of fault classification and achieve fault diagnosis for brushless DC motors. This method carries valuable implications for other varieties of BLDC motors.