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In this paper, a new method of fault diagnosis based on K-L transform and support vector machine(SVM) is presented on the basis of statistical learning theory and the feature analysis of vibrating signal of ball bearing. The key to the fault bearings diagnosis is feature extracting and feature classifying. Multidimensional correlated variable is converted into low dimensional independent eigenvector...
Although many works have been done to construct prediction models on yarn processing quality, the relation between spinning variables and yarn properties has not been established conclusively so far. Support vector machines (SVMs), based on statistical learning theory, are gaining applications in the areas of machine learning and pattern recognition because of the high accuracy and good generalization...
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