Fault detection and isolation of faults in a multivariate process with Bayesian network

Sylvain Verron (1) , Jing Li (2) , Teodor Tiplica (1)

1 LASQUO - Laboratoire en Sûreté de Fonctionnement, Qualité & Organisation
2 School of Computing, Informatics and Decision Systems Engineering

The main objective of this paper is to present a new method of detection and isolation with a Bayesian network. For that, a combination of two original works is made. The first one is the work of Li et al. [1] who proposed a causal decomposition of the T2 statistic. The second one is a previous work on the detection of fault with Bayesian networks [2], notably on the modeling of multivariate control charts in a Bayesian network. Thus, in the context of multivariate processes, we propose an original network structure allowing to decide if a fault has appeared in the process. This structure permits the isolation of the variables implicated in the fault. A particular interest of the method is the fact that the detection and the isolation can be made with a unique tool: a Bayesian network.

Domaines: Applications [stat.AP]; Intelligence artificielle [cs.AI]

Sylvain Verron, Jing Li, Teodor Tiplica. Fault detection and isolation of faults in a multivariate process with Bayesian network. Journal of Process Control, 2010, 20 (8), pp.902-911. ⟨10.1016/j.jprocont.2010.06.001⟩. ⟨hal-00516993⟩