Fault detection in industrial robot drive systems is a critical aspect of ensuring operational reliability and efficiency. To address the challenge of balancing accuracy and robustness in existing fault detection methods, this paper proposes an enhanced fault detection method based on the unscented Kalman filter (UKF). A comprehensive mathematical model of the brushless DC motor drive system is developed to provide a theoretical foundation for the design of subsequent fault detection methods. The conventional UKF estimation process is detailed, and its limitations in balancing estimation accuracy and robustness are addressed by introducing a dynamic, time-varying boundary layer. To further enhance detection performance, the method incorporates residual analysis using improved z-score and signal-to-noise ratio (SNR) metrics. Numerical simulations under both fault-free and faulty conditions demonstrate that the proposed approach achieves lower root mean square error (RMSE) in fault-free scenarios and provides reliable fault detection. These results highlight the potential of the proposed method to enhance the reliability and robustness of fault detection in industrial robot drive systems.
A Fri, study studied this question.