Fault diagnosis for steam separators based on parameter identification and CUSUM classification

Abstract

A method for diagnosing faults in steam separators is presented. Faults in the water level, water flow and steam flow sensors are analyzed. Precise models of the steam separator system are difficult to obtain, which makes the most common model-based fault detection and isolation approaches unapplicable. An identification-based method is used instead: parameters of the process are identified in real time, and the resulting data samples, which we denote as residuals, are used as inputs to a CUSUM-type classification scheme. It then decides if a fault is present, and if so, which one. In other words, residuals are first generated by parameter identification, and then evaluated by a modification of the CUSUM test. The choice of the CUSUM algorithm was motivated by its optimality with respect to detection delay. The identified parameters are assumed to be normally distributed. This assumption is experimentally verified: the true probability density functions (PDF) are estimated, and the performance of the detector based on these estimated PDFs is compared to that of the previous detector, based on the Gaussian PDF. The proposed method was tested on real-world data, obtained from the TEKO B1 Unit of the Kostolac Thermal Power Plant in Serbia. The results suggest extremely low probabilities of false alarm, missed detection and false isolation. As for detection delay, just one residual sample is needed for proper fault diagnosis in some cases, while 83 samples are needed in the worst-case scenario.