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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.