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This paper reports a new development based on the manifold learning and intelligent classifier for the nonlinear feature extraction and fault pattern recognition of mine hoists. The wavelet packet was firstly used to extract the statistic characteristics of the hoist vibrations to obtain the original feature space. Then the locally linear embedding (LLE) was employed to learn the underlying nonlinear manifold in the original feature space to select distinct features. Following, the support vector machine (SVM) was applied to the fault pattern recognition. The energy-entropy based genetic algorithm was used to optimize the SVM parameters. The experimental vibration data measured on a mine hoist test rig was used to evaluate the proposed method. The diagnosis results show that the proposed method is efficient for the mine hoist and can increase the detection rate by 2.5% or better when compared with existing diagnosis approaches.

A New Method for Fault Diagnosis of Mine Hoist based on Manifold Learning and Genetic Algorithm Optimized Support Vector Machine