Development of a neuro-fuzzy expert system for predictive maintenance

Gary G. Yen; Phayung Meesad

doi:10.1117/12.434233

20 July 2001 Development of a neuro-fuzzy expert system for predictive maintenance

Gary G. Yen, Phayung Meesad

Proceedings Volume 4389, Component and Systems Diagnostics, Prognosis, and Health Management; (2001) https://doi.org/10.1117/12.434233
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

Abstract

In this paper, a method for automatic constructing a fuzzy expert system from numerical data using the ILFN network and the Genetic Algorithm is presented. The Incremental Learning Fuzzy Neural (ILFN) network was developed for pattern classification applications. The ILFN network, employed fuzzy sets and neural network theory, is a fast, one-pass, on-line, and incremental nearing algorithm. After trained, the ILFN network stored numerical knowledge in hidden units, which can then be directly mapped into if-then rule bases. A knowledge base for fuzzy expert systems can then be extracted from the hidden units of the ILFN classifier. A genetic algorithm is then invoked, in an iterative manner, to reduce number of rules and select only important features of input patterns needed to provide to a fuzzy rule-based system. Three computer simulations using the Wisconsin breast cancer data set were performed. Using 400 patterns for training and 299 patterns for testing, the derived fuzzy expert system achieved 99.5% and 98.33% correct classification on the training set and the test set, respectively.

Citation Download Citation

Gary G. Yen and Phayung Meesad "Development of a neuro-fuzzy expert system for predictive maintenance", Proc. SPIE 4389, Component and Systems Diagnostics, Prognosis, and Health Management, (20 July 2001); https://doi.org/10.1117/12.434233

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