Fault Detection Based on Validated Model of Data Filtering Based Recursive Least Squares Algorithm For Box-Jenkins Systems

Abstract

In this paper, the data filtering based Recursive Least Squares algorithm (RLS) of linear Box-Jenkins systems is proposed for fault detection. The system is decomposed into two subsystems, one containing the parameters of the system model and the other containing the parameters of the noise model, and these parameters of the system model and the noise model are estimated. The model validation is tested using two statistical methods, histogram and mean square errors. The residual is generated based on the proposed algorithm to design the threshold and therefore, this design is used for fault detection. Simulation results are performed to illustrate the algorithm performance.