Bayesian Networks Approach for a Fault Detection and Isolation Case Study

Abstract

This paper presents a Fault Detection and Isolation (FDI) approach based on the use of Hybrid Dynamic Bayesian Networks (HDBN). The peculiarity of the proposed approach is that an analytical dynamic model of the process to be monitored is not required. Instead it is hypothesized that input/output measures performed on the considered process during different working conditions, including faults, are available. In the paper the proposed FDI approach is described and the performances are evaluated on synthetic and real data supplied by a standard benchmark consisting of an hydraulic actuators available in literature. The goodness of the proposed approach is assessed by using appropriate performance indices. An intercomparison between the BN approach and an other approach, namely a Multilayer Perceptron (MLP) neural network is given. Results show that the BN approach outperforms the MLP approach in some indices but it requires a high design and computational effort.