Plant monitoring and diagnosis by transient identification: the fuzzy approach

Plant monitoring and diagnosis are usually integrated as one process to detect and isolate suspect symptoms and use these symptoms to find the root cause of the failure. The research reported here enables two new plant monitoring and diagnosis frameworks/architectures, each of which employs multiple fuzzy rule-based systems in parallel and in sequence. By employing fuzzy sets and by constructing a decision concerning the normalcy of system behavior in stages, we are able to exploit far more information contained in the signals (one signal per sensor). The paper focuses on a new transient identification scheme using fuzzy logic. A fuzzy rule based transient identification system is implemented in MATLAB. The experiments demonstrate how traditional features such as WOLP and HAS, for transient identification can be fuzzified to improve the efficiency and performance of traditional classifiers used for the same purpose.