动态调整灰色关联分析方法在轴承早期退化在线识别中的应用  

A dynamically adjustment grey incidence analysis method and its application to online recognition of early degradation of bearing

作　　者：裴雪武 董绍江[1,2,3] 方能炜 邢镔 胡小林 Pei Xuewu;Dong Shaojiang;Fang Nengwei;Xing Bin;Hu Xiaolin(School of Mechanotronics and Vehicle Engineering,Chongqing Jiaotong University,Chongqing 400074,China;Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle,Ministry of Education,Chendu 610031,China;Chongqing Industrial Big Data Innovation Center Co.,Ltd.,Chongqing 400707,China)

机构地区：[1]重庆交通大学机电与车辆工程学院,重庆400074 [2]磁悬技术与磁浮列车教育部重点实验室,成都610031 [3]重庆工业大数据创新中心有限公司,重庆400707

出　　处：《仪器仪表学报》2023年第5期61-70,共10页Chinese Journal of Scientific Instrument

基　　金：国家自然科学基金(51775072);重庆市科技创新领军人才支持计划项目(CSTCCCXLJRC201920);重庆交通大学研究生科研创新资助项目(2021S0036);重庆市高校创新研究群体(CXQT20019);重庆市北碚区科学技术局技术创新与应用示范项目(2020-6)资助。

摘　　要：针对现有数据驱动型方法在滚动轴承早期退化识别中存在敏感度低、误警率高的问题,提出一种面向瞬态机械装备健康监测的动态调整灰色关联分析(DAGIA)方法。该方法首先采用希尔伯特(Hilbert)变换对滚动轴承振动数据进行幅度解调得到包络信号。为了削弱分辨系数取值的影响以凸显关联度值的区分程度,将可以表征轴承退化信息强弱的特征噪声能量比(FNER)指标引入传统灰色关联分析(TGIA)中动态调整分辨系数。然后,提取轴承运行初期的第一组数据作为参考数据,计算其余数据和参考数据的动态灰色关联度并构建轴承性能衰退指标。最后,根据正常样本并结合切比雪夫不等式设置控制线瞬态识别滚动轴承早期退化起始位置。利用IMS和XJTU-SY数据库完成对轴承早期退化瞬态识别,结果表明,所提方法可以瞬态识别轴承早期退化位置,误报警逼近于0,兼具敏感性和鲁棒性,有利于设备维护人员更好掌握滚动轴承的运行状态。The existing data-driven methods in the early detection of rolling bearing degradation have problems of low sensitivity and high false alarms.To address these issues,a dynamically adjustment grey incidence analysis(DAGIA)method for transient mechanical equipment health monitoring is proposed.First,the Hilbert transform is applied to demodulate the vibration data of the rolling bearing to obtain the envelope signal.To weaken the influence of the value of the resolution coefficient to highlight the degree of discrimination of the correlation value,the feature-to-noise energy ratio(FNER)method is introduced into the traditional grey incidence analysis(TGIA)to dynamically adjust the resolution coefficient,which can characterize the strength of bearing faults.Then,the first set of data is extracted at the initial stage of bearing operation as reference data.The dynamic grey incidence analysis is calculated between the remaining data and the reference data and the bearing performance degradation index is established.Finally,according to the normal samples and combined with Chebyshev′s inequality,the control line is set to identify the starting position of the early degradation of the rolling bearing.The IMS and XJTU-SY databases are used to complete the early degradation recognition of rolling bearings.The results show that the proposed method can accurately recognize the starting position of early degradation and the false alarm is close to 0.It has both sensitivity and robustness,which is beneficial for equipment maintenance personnel to better grasp the operating status of rolling bearings.

关 键 词：轴承 特征噪声能量比 动态调整灰色关联分析 性能衰退指标 早期退化在线瞬态识别 

分 类 号：TH17[机械工程—机械制造及自动化]