Centralized and decentralized process and sensor fault monitoring using data fusion based on adaptive extended Kalman filter algorithm

Abstract This paper presents an integrated design framework to utilize multi-sensor data fusion (MSDF) techniques for process monitoring enhancement to detect and diagnose sensor and process faults. Two different distributed and centralized architectures are presented to integrate the multi-sensor data based on extended Kalman filter (EKF) data fusion algorithm. The distributed integration architecture uses the state-vector fusion method, while the centralized integration architecture is based on the output augmented fusion (OAF) method. The usual approach in the classical EKF implementation is based on the assumption of constant diagonal matrices for both the process and measurement covariances. This inflexible constant covariance set-up may cause degradation in the EKF performance. A new adaptive modified EKF (AMEKF) algorithm has been developed to prevent the filter divergence and hence leading to an improved EKF estimation. A set of simulation studies have been conducted to demonstrate the performances of the proposed adaptive and non-adaptive process monitoring approaches on a continuous stirred tank reactor (CSTR) benchmark problem. The sensor fault studies include the sensor faults due to drift in calibration and drift in sensor degradation anomalies. Whereas, the process faults consist of four probable CSTR faults in cascaded single, double, triple and quadruple set-up.