Subspace aided data-driven design of robust fault detection and isolation systems

This paper deals with subspace method aided data-driven design of robust fault detection and isolation systems. The basic idea is to identify a primary form of residual generators directly from test data and then make use of performance indices to make uniform the design of different type robust residuals. Four algorithms are proposed to design fault detection, isolation and identification residual generators. Each of them can achieve robustness to unknown inputs and sensitivity to sensor or actuator faults. Their existence conditions and multi-fault identification problem are briefly analyzed as well and the application of the method proposed is illustrated by a simulation study on the vehicle lateral dynamic system.