This paper addresses the problem of improving state estimation of dynamic industrial processes in real time for single, double, triple and quadruple fault detection and diagnosis purposes using multi-sensor data fusion strategy. The proposed monitoring systems track the process states to infer its operating conditions utilizing a decentralized kalman filtering methodology based on state-vector fusion technique. The paper considers both the synchronous and asynchronous multi-sensor scenarios to explore their relevant data fusion problems. The performances of the resulting monitoring systems are investigated under the two possible cases of timedelayed measurements due to communication delay and multi-rate sensors. The state-vector data fusion technique is also adopted to integrate the individual state feature coming from the distributed extended kalman filter (EKFs) so as to extract the necessary global detection and diagnostic information. The feasibility and effectiveness of the presented algorithms are demonstrated through simulation studies on a continuous stirred tank reactor (CSTR) benchmark problem.
Safe and reliable operation of industrial chemical plants necessitates proper design and performance of instrumentation sensor networks. In this paper, a data reconciliation technique based on the unscented Kalman filter (UKF) is proposed to extend an instrumentation sensor network design approach to non-linear dynamic processes. Moreover, an efficient performance measure based on the root mean squared error (RMSE) of the estimated variables has been presented to evaluate each candidate instrumentation sensor network design. A simulated nonlinear continuous stirred tank reactor (CSTR) benchmark plant has been utilized to illustrate the effective capabilities of the proposed approach.
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