Enhanced fault diagnosis method using conditional Gaussian network for dynamic processes

Applying fault detection and diagnosis (FDD) technology to the process industry can help to detect faults in time and minimize their impact. The purpose of this study is to propose an enhanced fault diagnosis method under a Conditional Gaussian Network(CGN) efficient and suitable for dynamic processes fault monitoring. The key paths are as follows: first, a time series model is established for the process data and decomposed into time-dependent components and time-independent components; second, time-dependent components are discarded and time-independent components void of auto-correlation are considered instead of the original data to learn the CGN model. A numerical simulation case is used to illustrate the interest of our proposal. The effectiveness of the proposed method is further verified and compared on the Tennessee Eastman Process (TEP). The obtained results show that our method has high and better accuracies regarding the diagnosis of known and unknown faults in dynamic processes.