Using B-spline neural network to extract fuzzy rules for a centrifugal pump monitoring

In mechanical equipment monitoring tasks, fuzzy logic theory has been appli ed to situations where accurate mathematical models are unavailable or too complex to be established, but there may exist some obscure, subjective and empirical knowledge about the problem under investigation. Such kind of kn owledge is usually formalized as a set of fuzzy relationships (rules) on wh ich the entire fuzzy system is based upon. Sometimes, the fuzzy rules provi ded by human experts are only partial and rarely complete, while a set of s ystem input/output data are available. Under such situations, it is desirab le to extract fuzzy relationships from system data and combine human knowle dge and experience to form a complete and relevant set of fuzzy rules. This paper describes application of B-spline neural network to monitor centrifu gal pumps. A neuro-fuzzy approach has been established for extracting a set of fuzzy relationships from observation data, where B-spline neural networ k is employed to learn the internal mapping relations from a set of feature s/conditions of the pump. A general procedure has been setup using the basi c structure and learning mechanism of the network and finally, the network performance and results have been discussed.