A novel integrated fault diagnosis method of chemical processes based on deep learning and information propagation hysteresis analysis

Abstract

BackgroundTo guarantee the stationary operation of chemical plant, the anomaly symptoms of equipment should be discovered as soon as possible. Due to the limited ability of single fault diagnosis method in the increasingly complex industrial process, the integration of different methods has become a hot research direction. MethodsA three-layer fusion fault diagnosis method is proposed in this paper. This method consists of three layers. Time domain features of time series process variables are first extracted by sliding window. Then, the bidirectional long short-term memory network based on time domain feature extraction (T-BiLSTM) is formed to accomplish the online fault identification task. At last, the causalities of fault variables are obtained based on improved signed directed graph model with dynamic time warping (TD-SDG) to locate the fault sources. FindingsThe application to Tennessee Eastman (TE) shows that the average false positive rate (FPR) of T-BiLSTM is 1.98%, and the average fault diagnosis rate (FDR) is 96.6%. TD-SDG model realizes fault inference and location. This novel integrated fault diagnosis method thus can identify fault types and locate fault sources quickly based on graphical scenario object model with a high application capacity.