Multiple sensor fault diagnosis for non-linear and dynamic system by evolving approach

Reliability of sensor measurement is vital to assure the performance of complex and nonlinear industrial operation. In this paper, the problem of designing and development of a data-driven multiple sensor fault detection and isolation (MSFDI) algorithm for nonlinear processes is investigated. The proposed scheme is based on an evolving multi-Takagi Sugeno framework in which each sensor output is estimated using a model derived from the available input-output measurement. Our proposed MSFDI algorithm is applied to continuously stirred tank reactor sensor fault detection and isolation. Simulation results demonstrate and validate the performance capabilities of our proposed MSFDI algorithm.