Fault detection and identification using combination of EKF and neuro-fuzzy network applied to a chemical process (CSTR)

In this paper, a new algorithm is proposed for fault detection and identification (FDI) in a class of nonlinear systems by combining the extended Kalman filter (EKF) and neuro-fuzzy networks (NFNs). There is an abundance of the literature on fault diagnosis ranging from model-based methods to data-driven approaches that have advantages and drawbacks. One may employ the advantages of different approaches to develop a high-efficient method for fault diagnosis. Initially, an EKF is designed to estimate the system output and to generate accurate residuals by a mathematical model of the process. Then, an NFN is designed for making decision using the mean value of the residuals. The network assigns a locally linear model to each faulty condition of the system. The validity of the models is determined based on the fuzzy rules. Combining the introduced EKF and the introduced NFN causes the proposed method to be independent of pre-designing a bank of observers in the model-based methods. Moreover, there is no need for extracting the features from the signals without any physical insight as well as computational complexity in the data-driven techniques. The effectiveness of the proposed FDI scheme is verified by applying it to a chemical plant as the case study, namely, continuous stirred tank reactor process. Simulation results show that the proposed methodology is very effective to detect and identify the faults of the system in different faulty modes.