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.
Highlights
This paper has presented an effective remaining useful life (RUL) estimation method of BLDC motor by considering the generator output voltage as a health indicator
Motor current signature analysis (MCSA) is performed on motor current for both fault types to understand the fault characteristics and identify the faults at the earliest stage
Collected data for the entire lifecycle are normalized using moving average filtering and ground truth of the degradation is obtained as true RUL
Summary
Several studies have shown different approaches to detect and diagnose stator related faults in BLDC motor. B. PROPOSED METHOD In this paper, we propose a deep learning-based degradation estimation model to predict the RUL of BLDC motor. Generator voltage is produced from the mechanical output of motor and free from external environmental influences like heat, noise, and vibration This parameter will act as a proper diagnostics index to estimate the RUL in dynamic operating conditions. LSTM architecture based on attention mechanism is used to estimate the RUL of motor for both types of faults. Most frequently occurring stator-faults can be categorized as: (a) Inter-turn short circuit, and (b) Winding short-circuit In both cases, shorted sections create several complicated unbalances in motor operation that can develop severe faults in BLDC motor.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
