A generic framework for decision fusion in Fault Detection and Diagnosis

In this paper, we propose a unified framework that enables decisions fusion for applications dealing with multiple heterogeneous Fault Detection and Diagnosis (FDD) methods. This framework, which is a discrete Bayesian Network (BN), is generic and can encompass all FDD method, whether it requires an accurate model or historical data. The main issue concerns the integration of different decisions emanating from individual FDD methods in order to obtain more reliable results. The methodology is based on a theoretical learning of the BN parameters, according to the FDD objectives to be reached. The development leads to a multi-objective problem under constraints, which is solved with a lexicographic approach. The effectiveness of the proposed decision fusion approach is validated through the Tennessee Eastman Process (TEP), which represents a challenging industrial benchmark. The application demonstrates the viability of the approach and highlights its ability to ensure a significant improvement in FDD performances, by providing a high fault detection rate, a small false alarm rate and an effective strategy for the resolution of conflicts among different FDD methods.