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

Abstract

In mechanical equipment monitoring tasks, fuzzy logic theory has been applied 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 knowledge is usually formalized as a set of fuzzy relationships (rules) on which the entire fuzzy system is based upon. Sometimes, the fuzzy rules provided by human experts are only partial and rarely complete, while a set of system input/output data are available. Under such situations, it is desirable to extract fuzzy relationships from system data and combine human knowledge and experience to form a complete and relevant set of fuzzy rules. This paper describes application of B-spline neural network to monitor centrifugal pumps. A neuro-fuzzy approach has been established for extracting a set of fuzzy relationships from observation data, where B-spline neural network is employed to learn the internal mapping relations from a set of features/conditions of the pump. A general procedure has been setup using the basic structure and learning mechanism of the network and finally, the network performance and results have been discussed.