CLC number: TP242.2

Fault Detection of Industrial Robot Drive Systems: An Enhanced Unscented Kalman Filter Approach

工业机器人驱动系统故障检测：一种增强型UKF方法

¹ School of Internet of Things Engineering, Wuxi Taihu University, Wuxi 214000, Jiangsu, China
² Provincial Key (Construction) Laboratory of Intelligent Internet of Things Technology and Applications in Universities, Wuxi 214000, Jiangsu, China
³ School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213000, Jiangsu, China

Received: 18 December 2024

Abstract

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.

摘要

工业机器人驱动系统故障检测是确保其可靠高效运行的关键。针对现有故障检测方法中精度与鲁棒性难以平衡的问题，本文提出一种基于无迹卡尔曼滤波器(UKF)的增强型故障检测方法。通过建立无刷直流电机驱动系统的完整数学模型，为后续的故障检测方法设计提供理论支持。在阐述传统UKF的估计过程基础上，通过引入动态时变边界层来解决其估计精度与鲁棒性难以平衡的局限。所提方法融合了改进z-score和信噪比(SNR)的残差分析方法，以进一步提升故障检测性能。在无故障和故障工况下的数值仿真表明，该方法在无故障工况下具有更低均方根误差(RMSE)，且能实现可靠故障检测。研究结果验证了此方法在提升工业机器人驱动系统故障检测可靠性和鲁棒性方面的潜力。