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• W2058264740 abstract "Coal mines require various kinds of machinery. The fault diagnosis of this equipment has a great impact on mine production. The problem of incorrect classification of noisy data by traditional support vector machines is addressed by a proposed Probability Least Squares Support Vector Classification Machine (PLSSVCM). Samples that cannot be definitely determined as belonging to one class will be assigned to a class by the PLSSVCM based on a probability value. This gives the classification results both a qualitative explanation and a quantitative evaluation. Simulation results of a fault diagnosis show that the correct rate of the PLSSVCM is 100%. Even though samples are noisy, the PLSSVCM still can effectively realize multi-class fault diagnosis of a roller bearing. The generalization property of the PLSSVCM is better than that of a neural network and a LSSVCM." @default.
• W2058264740 created "2016-06-24" @default.
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• W2058264740 date "2010-11-01" @default.
• W2058264740 modified "2023-09-25" @default.
• W2058264740 title "Fault diagnosis using a probability least squares support vector classification machine" @default.
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• W2058264740 doi "https://doi.org/10.1016/s1674-5264(09)60307-0" @default.
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