摘要：AbstractTimely detection and diagnosis of abnormal events play an important role in the safe operation of any chemical plant. To maintain satisfactory performance of the process plant in the face of faults, it becomes important to identify faults online soon after they occur and implement appropriate corrective measures. In this work, it is proposed to use a moving window parameter estimator developed by Huang et al. (2010), to carry out leak detection in a storage tank. A leak occurring at a bottom of a tank is modelled as a change in an appropriate model parameter. The moving window parameter estimator, which uses extended kalman filter for state estimation, is employed for simultaneous estimation of the states and the parameters representing the leaks. Eectiveness of the moving window estimator is demonstrated by conducting simulation studies on the benchmark quadruple tank system (Johansson (2000)). The results obtained through simulation are further validated by conducting experimental studies on the quadruple tank experimental setup available at I.I.T. Bombay. The efficacy of the moving window estimator is compared with that of the conventional augmented EKF approach. While both the estimators are able to identify the leak flow(s) in simulation as well as in the experimental studies, the moving window estimator is relatively easy to tune and performs much better than the augmented EKF.
关键词：KeywordsFault DiagnosisState and Parameter Estimation
