Fault Diagnosis of Conveyance Machine Based on Fuzzy Support Vector

Abstract:

Aiming random and nonlinearity for conveyance machine of rubber belt in mine, a method of fault diagnosis is presented which fusion of fuzzy theory and support vector machine (FSVM). According to the coal mine safety rules of the regulation, the conveyance machine servicing are deduced eleven faults after analyzing practice statistic data, here, we consider some are fuzzy that the statistic data are divided to the normal kind or fault kind, but some are definite that the statistic data possibility are belong to same kind fault, accordingly, the fuzzy support vectors is established. Farther, two kernel functions of FSVM is made for seeking the problem of random and nonlinearity, which are RBF and TANH. According to the random statistic data and the study sample, analyzing the effect of expense and kernel function in selecting different parameters, the unitary constant is ascertained, next, the FSVM kernel function of fault diagnosis multi-class rules are established, then, this method availability is proved using test data and simulation.

• Periodical: Applied Mechanics and Materials (Volumes 135-136)
• Pages: 547-552
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.135-136.547
• Online since: October 2011
• Authors: Yuan Bin Hou, Ning Li, Fan Guo, Jing Chen
• Keywords: Conveyance Machine, Fault Diagnosis, FSVM Kernel Function, Multi-Class Rules

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