A HYBRID ANN-ES SYSTEM FOR DYNAMIC FAULT-DIAGNOSIS OF HYDROCRACKING PROCESS

In this paper, the wavelet-sigmoid basis neural network(WSBN) proposed by authors earlier is with expert system(ES) for dynamic fault diagno sis(DFD). Meanwhile, a two-level integration strategy(TLIS) for DFD is also presented. At the first level of TLIS, a WSBN detects the fault sources through data from the dynamic processes, and outputs the corre sponding fault degrees. At the second level, ES interprets the results from the WSBN and predicts the time period for fully developing the f aults and the product qualities as well as the reactor states at the m oment when the fault is fully developed by using a dynamic simulation package. If the predicted values exceed their acceptable ranges, then ES gives some related proposals for removing the fault causes or cance ling their effects by using the reasoning context consisted of product ion rules. DFD is applied to a hydrocracking process showing the effic iency of the TLIS.