Identification of probabilistic graphical network model for root-cause diagnosis in industrial processes

Identification of faults in process systems can be based purely on measurement (e.g. PCA), or can exploit knowledge of process model structure to construct a causal network. This work introduces a method to identify most likely causal network in cases when process model is not known. An incidence matrix, showing location of measurements in the plant network structure, and historical process data are used to identify the optimal causal network …