Fault Intelligent Diagnosis of Coal Mine Hoist Based on GCFNN

Li Li Dong; Qing Qing Ding

doi:10.4028/www.scientific.net/AMR.634-638.3716

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 634-638Fault Intelligent Diagnosis of Coal Mine Hoist...

Fault Intelligent Diagnosis of Coal Mine Hoist Based on GCFNN

Abstract:

Equipment running subtle condition can’t be clearly expressed by clustering result of explicit affiliation in the fuzzy neural network fault diagnosis. In order to solve the problems in the present, the integration of grey clustering theory and fuzzy neural network was researched, and the fault intelligent diagnosis methods based on grey clustering fuzzy neural network (GCFNN) was proposed, the structure and the algorithm of GCFNN were designed, and the model of GCFNN was established. In coal mine hoist hydraulic subsystem fault diagnosis as an example, the feasibility and validity of the method is simulated and verified. The experiment results show that GCFNN can make a correct diagnosis, express more detailed equipment condition information. The method proposed provides basis for the maintenance of the mine hoist, and provides a new approach for the fault diagnosis of the other mine equipment.

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Periodical:

Advanced Materials Research (Volumes 634-638)

Pages:

3716-3720

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.634-638.3716

Citation:

Cite this paper

Online since:

January 2013

Authors:

Li Li Dong, Qing Qing Ding

Keywords:

Coal Mine Hoist, Fault Intelligent Diagnosis, Fuzzy Neural Network (FNN), Grey Clustering Fuzzy Neural Network, Grey Clustering Theory

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