【摘要】 为解决滚动轴承可靠性难以评估的问题,提出了一种基于改进Logistic回归模型(improved logistic regression model,简称ILRM)的滚动轴承可靠性评估方法。首先,计算滚动轴承的时域、频域和时频域特征,选出有效特征组成相对高维特征集;其次,利用主元分析(principal component analysis,简称PCA)选取贡献率大于95%的主元,作为改进Logistics回归模型的协变量;最后,利用改进Logistic模型求取滚动轴承的可靠度并绘制可靠度曲线。该方法可以提取轴承退化的有效特征量;兼顾轴承的退化趋势,能够真实反映轴承的状态;消除信号随机波动对可靠度预测的影响。通过辛辛那提大学智能维护中心(intelligent maintenance systems,简称IMS)滚动轴承全寿命试验,验证了该方法的有效性。更多还原

【Abstract】 The reliability evaluation of the rolling bearing is crucially important in improving the reliability of mechanical equipment and saving maintenance cost.A novel reliability evaluation method is proposed based on improved logistic regression model(ILRM)to solve the problem that the reliability of rolling bearing is difficult to estimate.First,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.Second,the principal components which can accurately reflect the performance degradation process are obtained by principal component analysis.Then,the principle components are used as the covariates of ILRM.Finally,the covariates are brought into ILRM to obtain the reliability of the rolling bearing.The method can be used to extract the effective characteristics of bearing degradation,and can reflect the state of bearing,and eliminate the influence of random fluctuation of signal on the reliability evaluation.The results verified by intelligent maintenance systems full life test of rolling bearing show that the method can accurately evaluate the reliability of rolling bearings.更多还原