基于KPCA和WPHM的滚动轴承可靠性评估与寿命预测

为了评估滚动轴承的可靠性和预测剩余使用寿命,选取能够反映性能退化过程的特征参数作为寿命预测模型的输入参数,提出一种基于核主元分析(kernel principal component analysis,简称KPCA)和威布尔比例故障率模型(Weibull proportional hazards model,简称WPHM)的方法。首先,提取滚动轴承全寿命周期的时域、频域及时频域等多特征参数,从中筛选出有效的特征参数,构建高维相对特征集;其次,进行核主元分析,选取能够反映轴承全寿命周期性能退化过程的核主元,进而作为WPHM的协变量来进行可靠性评估和剩余寿命预测。通过滚动轴承全寿命试验,验证了该方法能够对轴承进行准确的可靠性评估和剩余寿命预测,以提供及时的维修决策。同时,由于提取的是相对特征,降低了同种轴承间在制造、安装及工况的差异,增强了该方法的适用性和稳定性。

Rolling Bearing Reliability Assessment and Life Prediction Based on KPCA and WPHM

The remaining useful life (RUL) prediction of rolling bearing is significant for proactive maintenance of equipment, and selecting the features which can accurately reflect the performance degradation process as the inputs of the life prediction model is the premise of accurate RUL prediction. A novel method based on kernel principal component analysis (KPCA) and Weibull proportional hazard model (WPHM), is proposed to assess the reliability and predict the RUL of the rolling bearing. High relative feature set is constructed by selecting the effective features through extracting the time domain, frequency domain and time-frequency domain features of lifetime bearing. The kernel principal components (KPCs) which can accurately reflect the performance degradation process are obtained by KPCA. Then the KPCs are used as the covariates of WPHM to assess the reliability and predict the RUL. An example of bearing test is provided to demonstrate that this method can accurately assess the reliability and predict the RUL to provide timely maintenance resolution. Meanwhile, as the relative features are extracted, the differences in manufacturing, installation and working condition of the same type bearings are reduced, which enhances the practicability and stability of the method.