A Robust Data-Driven Sensor Fault Detection Method for Traction Drive Systems

This article addresses the fault detection (FD) problem for traction drive systems with the consideration of noises with inexact probability distributions. A traction drive system with sensor faults is first described as a dynamic process. Following the idea of distributionally robust optimization (DRO), an ambiguity set based on the Wasserstein metric is employed to tackle the distributional uncertainties associated with signals obtained in traction drive systems. By utilizing this ambiguity set and the DRO framework, a robust FD system is developed, ensuring reliable detection performance under the uncertainty of the noise distribution. The reliability and effectiveness of the developed approach are confirmed through an experimental study on an actual traction drive system.