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- W2364919663 abstract "A hierarchic classification diagnosis model, based on fuzzy membership grade and rules is being proposed. First of all, the hierarchic classification method is discussed by taking steam turbine blading faults as the object concerned, and a fault diagnosis tree, based on decomposition of structure and fault events, is set up. Next, in accordance with actual conditions given by the thermal parameters, a fuzzy membership grade function is chosen and its algorithm fixed. Finally the advantages of these two methods are synthesized in the design of the fault nodes' organizational structure of knowledge with the knowledge repertory of each fault node divided into different compartments for storing behavior parameters, original evidences, proof patterns, neural network information, fuzzy rule bank and index knowledge. This model, which not only saves searching work for fault decisions, but also expresses fuzzy and uncertain knowledge by the fuzzy neural network's weight and acquires knowledge through the fuzzy neural network's training, thereby solving the bottleneck of knowledge acquisition. Practical probation of fault diagnosing capability shows that the model suits the purpose of blading fault diagnosis quite well. Figs2, tables4 and refs2." @default.
- W2364919663 created "2016-06-24" @default.
- W2364919663 creator A5021858873 @default.
- W2364919663 date "2005-01-01" @default.
- W2364919663 modified "2023-09-23" @default.
- W2364919663 title "Hierarchic Classification Diagnosis Models Based on Membership Grade and Rules" @default.
- W2364919663 hasPublicationYear "2005" @default.
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