Based on the measured vibration status of high-speed railway bearings, a dynamic prediction model of reliability is proposed to realize the real-time monitoring and forecasting of the performance reliability of high-speed railway bearings. The vibration variation intensity of different service periods is quantified and analysed to measure the degree of the potential variation of the bearings. The five closest and updated variation intensities are obtained with linear fitting by the bootstrap-least squares method, and the maximum entropy principle is utilized to forecast the true value and upper and lower bounds of the variation intensity in the subsequent period. Then, the dynamic prediction of reliability for high-speed railway bearings is realized according to the Poisson process. Finally, reliability forecast values are compared with actual values to verify the accuracy and feasibility of the dynamic model. The investigation shows that the maximum relative error of the predicted reliability value is only 5.11% and that the proposed model can precisely predict the performance reliability of the high-speed railway bearings and give timely warning before the reliability decreases or a malignant accident occurs.
High-speed railway bearings Performance reliability Variation intensity Bootstrap-least squares method Maximum entropy principle Poisson process
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This project is supported by the National Natural Science Foundation of China (Grant No. 51475144) and the Natural Science Foundation of Henan Province of China (Grant No. 162300410065).
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