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Prediction of bearing life and reliability test
发布时间:2021-07-29浏览次数:6


During the whole life cycle operation of the bearing, it is likely to be affected by factors such as high temperature, poor lubrication, improper assembly and foreign matter intrusion, resulting in bearing damage and failure. Because the bearing life is very discrete, the maximum life and minimum life of a batch of bearings with the same structure, the same material, the same heat treatment and the same processing method are dozens of times or even more different under the same working conditions. The traditional mathematical statistics method shows that the bearing life test data approximately conforms to Weibull distribution or lognormal distribution, but it is still difficult to predict in the actual working conditions. Therefore, the effective processing of bearing life test data is particularly important. Research institutions at home and abroad also actively carry out relevant research on bearing life test data. Saxena et al. Used the power spectral density parameter as the performance degradation index of rolling bearing to predict the remaining service life of bearing, and its density parameter can diagnose the location and degree of fault. Xiao Ting et al. Used kurtosis and multi domain feature set as trend prediction indicators, which can not only effectively reflect the running state of the bearing, but also predict the performance degradation trend of the bearing. Banjevic et al. Predicted the reliability function and residual life of the equipment using the proportional risk model, and took the covariate at a certain time as the benchmark to predict its residual life. Based on previous studies, kacpnynski proposed a prediction model combining monitoring data with material parameters, and used the model to predict the life of rolling bearing. Kimotho et al. Proposed a hybrid differential evolution particle swarm optimization (de-pso) algorithm to optimize the prediction method of kernel function and penalty parameters of support vector machine, which improved the classification accuracy and residual life prediction accuracy of support vector machine, and verified it with NASA standard bearing fault data. Orsagh et al. Predicted the initial time of fatigue spalling failure of rolling bearing by using Yu Harris model, and predicted the failure time of rolling bearing by using kotzalas Harris model. Panigrahi proposed a diffusion particle swarm optimization (DPSO) algorithm to solve the problem of maximum likelihood function estimation in the study of bearing performance degradation, and achieved good prediction results.


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