Fault Diagnosis for a MSF using Bayesian Networks

TARIFA ENRIQUE E; NÚÑEZ ÁLVARO F; FRANCO SAMUEL; MUSSATI SERGIO

King Hussein Bin Talal Convention Center, Dead Sea, Jordan

Conferencia; Euromed 2008: Desalination Cooperation among Mediterranean Countries of Europe and the MENA Region; 2008

This work outlines the development of a fault diagnostic system for a multi-stage flash (MSF) desalination plant using Bayesian networks (BNs). This diagnostic system processes the plant data to determine whether the process state is normal or not. In the latter case, the diagnostic system determines the cause of the abnormal process state; namely, it finds out which is the fault that is affecting the supervised process. A BN is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data analysis. A BN readily handles situations where some data entries are missing. It is also an ideal representation for combining prior knowledge (which often comes in causal form) and data because the model has both a causal and probabilistic semantics. Bayesian statistical methods in conjunction with BNs offer an efficient approach for avoiding the overfitting of data.