A deep belief network based fault diagnosis model for complex chemical processes

Data-driven methods have been regarded as desirable methods for fault detection and diagnosis (FDD) of practical chemical processes. However, with the big data era coming, how to effectively extract and present fault features is one of the keys to successful industrial applications of FDD technologies. In this paper, an extensible deep belief network (DBN) based fault diagnosis model is proposed. Individual fault features in both spatial and temporal domains are extracted by DBN sub-networks, aided by the mutual information technology. A global two-layer back-propagation network is trained and used for fault classification. In the final part of this paper, the benchmarked Tennessee Eastman process is utilized to illustrate the performance of the DBN based fault diagnosis model.