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国际刊号:1004-6801
国内刊号:32-1361/V
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  • 国内刊号:32-1361/V
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滚动轴承性能退化的时序多元状态估计方法
Performance Degradation Assessment of Rolling Bearing Based on AR Model and Multivariate State Estimation Technique
  
DOI:10.16450/j.cnki.issn.1004?6801.2021.06.008
中文关键词:  AR模型  多元状态估计  滚动轴承  性能退化评估
英文关键词:autoregressive model (AR)  multivariate state estimation technique (MSET)  rolling bearing  performance degradation assessment
基金项目:国家自然科学基金资助项目(51665013,51865130);江西省自然科学基金资助项目(20161BAB216134,20171BAB206028,20152ACB21020);江西省研究生创新资金资助项目(YC2019?S243)
作者单位
张龙1, 吴荣真1, 周建民1, 易剑昱1, 徐天鹏1, 王良1, 邹 孟2 (1.华东交通大学机电与车辆工程学院 南昌330013)(2.中国铁路南昌局集团有限公司南昌车辆段 南昌330201) 
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中文摘要:
      滚动轴承性能退化评估是预诊断的提前和基础,对在役滚动轴承实施在线状态监测和性能退化评估具有重要意义。针对概率相似度量评估方法存在模型复杂、容易过早饱和等现象,提出一种基于自回归时序 (autoregressive model,简称AR)模型和多元状态估计(multivariate state estimation technique, 简称MSET)的滚动轴承性能在线评估方法,其中AR模型用于提取轴承振动信号的状态特征,MSET模型用于重构AR模型系数。首先,提取正常运行状态下振动信号的AR模型系数构建MSET模型的历史记忆矩阵;其次,将待测信号的AR系数作为观测向量输入MSET模型中得到重构后的估计向量;最后,由原始AR系数和重构AR系数分别构造自回归模型,并各自完成对待测信号的时序建模,将两自回归模型所得残差序列的均方根值之差作为性能劣化程度指标。离散实验数据和全寿命疲劳实验数据分析结果表明,该方法能够有效检测早期故障,且具有与轴承故障发展趋势一致性更好等优点。
英文摘要:
      As the advance and foundation of prognostics, the bearing performance degradation assessment (PDA) is of great significance for online condition monitoring. Aiming at the problems that similarity-based methods are complex and time consuming, an online PDA method for rolling bearings is proposed based on autoregressive model (AR) and multivariate state estimation technique (MSET). The coefficients of the AR model serve as feature vectors to depict bearing performance states and the MSET model is used to reconstruct AR coefficients. For that purpose, firstly, the historical memory matrix of the MSET model is constructed with the AR coefficients of vibration signals under normal operation, and then the AR coefficients of the signals under consideration are input into the MSET model as observation vectors to obtain the reconstructed estimation vectors. By inputting the signal into the two autoregressive models, which are composed of original AR coefficients and reconstructed AR coefficients, respectively, the corresponding residual sequences are obtained. Finally, the performance degradation index is constructed by exploiting the difference between the root mean square values of the two residual sequences. Hence, artificially induced defects and run-to-failure data set from rolling bearings are processed to demonstrate the advantages of the method in terms of the trendability, consistency and sensitivity of early failure.
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