Fuzzy Rule Set Based Engine Fault Diagnosis

Abstract

Based on a fuzzy match method of fuzzy rule sets which are a series of fuzzy neural networks, a system framework used for the engine fault diagnosis is proposed in this paper. This fault diagnosis system consists of five parts, including the extraction of fuzzy rules, fuzzy reference rule sets, a fuzzy rule set to be detected, the fuzzy match module of fuzzy rule sets and the diagnosis logic module. The extraction of fuzzy rules involves two steps: step 1 adaptively divides the whole space of the trained data into the subspaces in the form of hypersphere, which is expected efficiently to work out the recognition questions in the high dimension space; step 2 generates a fuzzy rule in each sample subspace and calculates the membership degree of each fuzzy rule. This paper specially makes extension of the conception of the fuzzy rule for resolving the contradictions among the generated fuzzy rules. The fuzzy rule is divided into the fuzzy reference rule set and the fuzzy rule set to be detected. Many fuzzy reference rule sets are obtained by the extraction module of fuzzy rules for the offline learning, and a fuzzy rule set to be detected is online formed while the monitoring process is going on. With the beliefs estimated from the fuzzy match process of fuzzy rule sets, which indicate the existence of working classes in the plant, the diagnosis logic module can export fault detection time, fault isolation time, fault type and fault degree. The simulation researches of the fault diagnosis in a 2000N space propulsion system demonstrate the superior qualities of the fault diagnosis method on the basis of the fuzzy match of the fuzzy rule sets.