Fuzzy Logic based Automated Transient Identification for Plant Monitoring

Plant monitoring and diagnosis are usually integrated as one process to detect and isolate suspicious symptoms and use these to identify the root cause of the failure [1, 2]. The research reported here enables a new plant monitoring and diagnosis framework that employs multiple fuzzy rule-based decision-support system at different diagnosing stages. By employing fuzzy sets and by constructing a decision concerning the normalcy of system behavior in stages, we are able to exploit far more detailed hierarchical information contained in the signals. The fuzzy rule based transient identification system is implemented in MATLAB using its fuzzy logic toolbox. The paper describes in detail a new fuzzified transient behavior identification scheme. The experiments results demonstrate how traditional features such as those used in wavelet online pre-processing (WOLP) [10] and highly autonomous sensor (HAS) [19] for transient behavior identification can be fuzzified to improve the efficiency and performance of traditional monitoring and diagnosis used for the same purpose.