Application of a Neural Fuzzy System with Rule Extraction to Fault Detection and Diagnosis

Abstract

In this paper, the fuzzy min–max (FMM) neural network is integrated with a rule extraction algorithm, and the resulting network is applied to a real-world fault detection and diagnosis task in complex industrial processes. With the rule extraction capability, the FMM network is able to overcome the “black-box” phenomenon by justifying its predictions using fuzzy if–then rules that are comprehensible to domain users. To assess the effectiveness of the FMM network, real sensor measurements are collected and used for detecting and diagnosing the heat transfer and tube blockage conditions of a circulating water (CW) system in a power generation plant. The FMM network parameters are systematically varied and tested, with the results explained. Bootstrapping is employed to quantify stability of the network performance statistically. The extracted rules are found to be compatible with the domain information as well as the opinions of domain experts who are involved in the maintenance of the CW system. Implications of the FMM network with the rule extraction facility as an intelligent and useful fault detection and diagnosis tool are discussed.