Remaining useful life estimation of BLDC motor considering voltage degradation and attention-based neural network

Abstract

Brushless DC motor, also referred to as BLDC motor, has been a widely used electric machine due to its excellent performance over conventional DC motors. Due to complex operating conditions and overloading, several irregularities can take place in a motor. Stator related faults are among the most commonly occurring faults in BLDC motor. With an initial raise in local heating, a fault in the stator can largely reduce motor efficiency and account for the entire system breakdown. In this study, we present a deep learning-based approach to estimate the remaining useful life (RUL) of BLDC motor affected by different stator related faults. To analyze the motor health degradation, we have investigated two types of stator faults namely inter-turn fault (ITF) and winding short-circuit fault (WSC). A generator was coupled with the motor and using an average value rectifier (AVR), generator's output voltage was monitored for the entire lifecycle. A proven neural network for effective sequence modeling, recurrent neural network (RNN) is selected to train the voltage degradation data. For a better estimation of nonlinear trends, long-short term memory (LSTM) with attention mechanism is chosen to make predictions of the motor RUL for both types of faults. The main concern that encourages authors of this paper is the proposed method can be used for the real-time condition monitoring and health state estimation of BLDC motors. Also, the proposed AVR-LSTM method is not affected by environmental influences, making it suitable for diverse operating conditions.