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《Journal of University of Science and Technology Beijing》 2006-07
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Fault diagnosis system based on ICA feature

QU Wei, LIU Heping, ZHANG DezhengInformation Engineering School, University of Science and Technology Beijing, Beijing 100083, China  
To overcome the difficulty of complex background in mining machine fault diagnosis, a fault diagnosis system based on independent component analysis (ICA) and vector quantization (VQ) was developed. A fault sound ICA model was presented to get the fault sound feature bases with ICA algorithms in extracting nature images and continuous speech features. One ICA separated the sounds from different parts of the machine and the other extracted the feature basis of fault sound. The coefficients of the basis were used in designing codebooks. The diagnosis accuracy of this system is 96.8% in the experiment with the realistic mine machine fault data, so the ICA-VQ is a high efficient fault diagnosis system.
【CateGory Index】: TP277
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