Fault diagnosis for high order LTI systems based on model decomposition

Fault detection observer and fault estimation filter are the main tools for the model based fault diagnosis approach. The dimension of the observer gain normally depends on the system order and the system output dimension. The fault estimation filter traditionally has the same system dimension of the monitored system. For high order systems, these methods have the potential problems such as parameter optimization and the real implementation on-board for applications. In this paper, the system dynamical model is first decomposed into a special structure. With the new model, a fault detection system can be designed such that only the residuals with the same dimension as the size of the faults are sensitive to the faults. The rest residuals are totally decoupled from the faults. A lower order (with the same size of the fault) fault estimation filter design approach is proposed. Further, the design of a static fault estimation matrix is presented for further improving the fault estimation precision. The proposed method is demonstrated by a simulation example.