准确预测滚动轴承的剩余使用寿命(remaining useful life,RUL)对机械设备安全可靠运行有着至关重要的作用，针对滚动轴承寿命预测中存在的
未能准确区分滚动轴承退化阶段与如何有效地利用历史退化数据与实时监测数据等问题，提出了一种SKF(switching Kalman filters)、KF(Kalman filters)和Bayes结合的滚动轴承性能退化建模与剩余使用寿命预测方法。结合滚动轴承振动信号性能监测数据，采用SKF方法识别出轴承性能退化的变点；利用随机效应指数退化模型描述轴承性能退化过程，结合同类轴承性能数据给出模型未知参数极大似然估计；利用KF单步预测对当前时刻监测数据进行修正，基于Bayes方法对模型中的随机参数进行实时更新，推导出轴承剩余使用寿命分布模型，计算滚动轴承剩余使用寿命；通过对滚动轴承试验数据分析，验证了该方法的适用性和有效性。
Abstract
Accurate prediction for residual life of rolling bearing plays an important role in safe and reliable operation of mechanical equipment. Here, aiming at problems in life prediction of rolling bearing, such as, not being able to accurately distinguish degradation stage of rolling bearing and how to effectively use historical degradation data and real-time monitoring data, a method for rolling bearing performance degradation modeling and remaining useful life (RUL) prediction based on combination of switching Kalman filters(SKF) recognition, Kalman filters (KF) single step prediction and Bayes update was proposed. Firstly, combined with performance monitoring data of rolling bearing vibration signals, SKF method was used to identify the change point of bearing performance degradation. Secondly, the random effect exponential degradation model was used to describe the process of bearing performance degradation, and the maximum likelihood estimation of the model’s unknown parameters was given based on the performance data of the same kind bearings. Then, KF single-step prediction was used to modify the monitoring data at the present moment, and random parameters in the model were updated in real time based on Bayes method to derive the bearing residual life distribution model, and calculate the residual life of rolling bearing. Finally, the applicability and effectiveness of the proposed method were verified through analyzing test data of rolling bearing.
关键词
滚动轴承 /
剩余使用寿命(RUL)预测 /
SKF识别 /
KF单步预测 /
Bayes更新
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Key words
rolling bearing /
remaining life useful (RUL) prediction /
switching Kalman filters (SKF) recognition /
Kalman filters (KF) single step prediction /
Bayes update
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