An Improved Data-Driven Scheme of Robust Fault Detection for Traction Drive Systems

This article addresses the fault detection (FD) problem for traction drive systems with stochastic noises and deterministic disturbances. A traction drive system with sensor and actuator faults is first described as a dynamic process. Then, the disturbance-decoupling residual generator is developed by constructing a subspace for deterministic disturbances. Based on the generated residual signals, the corresponding ambiguity sets are constructed to characterize the distributional uncertainties of noises. Moreover, the design of the target FD system is formulated as a distributionally robust optimization (DRO) problem. By solving the DRO problem, a robust FD approach is developed. It is worth noting that this method not only delivers satisfactory detection performances, but also enhances robustness against both deterministic disturbances and distributional uncertainties of stochastic noises. The reliability and validity of the developed method are illustrated by a numerical simulation and an experimental study on an actual traction drive system.