A New Intelligent Diagnostic Method for Machine Maintenance

Abstract

AbstractFuzzy neural networks display good capacity for self-adaptation and self-learning, and wavelet transformation or analysis reveals time frequency location characteristics and a multi-scale ability. Inspired by these advantages, a new intelligent diagnostic method for machine maintenance, wavelet fuzzy neural network (WFNN), is proposed in this paper. This new intelligent diagnostic method uses wavelet basis function as a membership function whose shape can be adjusted on line so that the networks have better learning and adaptive ability. An on-line learning algorithm is applied to automatically construct the wavelet fuzzy neural network. There are no rules initially in the wavelet fuzzy neural network, they are created and adapted as on-line learning proceeds via simultaneous structure and parameter learning. The advantages of this learning algorithm are that it converges quickly and the obtained fuzzy rules are more precise. The results of simulation show that this new intelligent diagnostic method has the advantages of a faster learning rate and higher diagnostic precision.KeywordsMembership FunctionFuzzy RuleFault DiagnosisFuzzy Inference SystemFuzzy Neural NetworkThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.