Abstract
A novel knowledge-based fuzzy neural network (KBFNN) for fault diagnosis is presented. Crude rules were extracted and the corresponding dependent factors and antecedent coverage factors were calculated firstly from the diagnostic sample based on rough sets theory. Then the number of rules was used to construct partially the structure of a fuzzy neural network and those factors were implemented as initial weights, with fuzzy output parameters being optimized by genetic algorithm. Such fuzzy neural network was called KBFNN. This KBFNN was utilized to identify typical faults of rotating machinery. Diagnostic results show that it has those merits of shorter training time and higher right diagnostic level compared to general fuzzy neural networks.
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Project supported by the National Major Science and Technology Foundation of China during the 10th Five-Year Plan Period (No.2001BA204B05-KHK Z0009)
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Li, Rq., Chen, J. & Wu, X. Fault diagnosis of rotating machinery using knowledge-based fuzzy neural network. Appl Math Mech 27, 99–108 (2006). https://doi.org/10.1007/s10483-006-0113-1
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DOI: https://doi.org/10.1007/s10483-006-0113-1