滚动轴承寿命动态预测新方法

由于传统可靠性分析方法是基于大量的失效试验数据和经验知识建立的静态模型,无法实现滚动轴承退化过程的跟踪以及准确进行可靠性评估和寿命预测,提出了基于主成分分析(principal component analysis,简称PCA)和相空间重构的滚动轴承寿命动态预测方法。首先,通过PCA将实时监测的多个滚动轴承性能指标进行融合;其次,使用相空间重构技术实现当前退化过程和历史退化过程的对比,得到寿命预测值,并结合历史失效时间进行统计推断,得到更准确的平均寿命。随着观测样本的不断积累,可实现平均寿命的动态更新。试验结果表明,本研究提出的动态寿命预测模型能够实时预测滚动轴承的寿命,具有较强的工程实用价值。

Method of Dynamic Life Prediction of Rolling Bearing

Research of the reliability evaluation and life prediction technology is of great significance to preventing faults and supporting predictive maintenance during rolling bearing running. The traditional reliability analysis methods with a static model based on large numbers of failure date and empirical knowledge are unable to track the degradation process of rolling bearing and accurate reliability assessment and life prediction. In this thesis, the dynamic reliability analysis model is established based on the principal component analysis (PCA) and phase space reconstruction. The real-time monitoring parameters are fused through PCA, and then the predictive value of life is obtained by comparing the current degradation process with the historical degradation process. With the continuous accumulation of observed samples, the life of rolling bearing can be updated. The experimental result shows that the dynamic life prediction model proposed in this paper can predict the life of the rolling bearing in real time.