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Real-time residual life prediction based on kernel density estimation considering abrupt change point detection
Measurement Science and Technology ( IF 2.4 ) Pub Date : 2020-09-10 , DOI: 10.1088/1361-6501/ab8fed
Weizhen Zhang 1 , Hui Shi 1 , Jianchao Zeng 1, 2 , Yunzheng Zhang 1
Affiliation  

The existence of abrupt change points in mechanical equipment degradation leads to inaccuracies in the prediction of its residual life. We propose a real-time residual life prediction method based on kernel density estimation (KDE) considering the influence of abrupt change points. First, a non-parametric cumulative sum method is used to detect abrupt change points in the degradation process. Then, integral mean square error is used to determine the abrupt change in the sample number that affects the accuracy of KDE. The weight coefficient is adaptively allocated according to the change in sample numbers relative to the minimum sample number before and after the abrupt change point in real-time monitoring. This method considers abrupt change states in the degradation process and uses KDE, which does not make model structure assumptions or parameter estimations for the degradation process. Finally, the effectiveness of this method is verified using the gear wear degradation data ...

中文翻译:

基于核密度估计并考虑突变点检测的实时剩余寿命预测

机械设备退化中突变点的存在会导致其剩余寿命的预测不准确。考虑到突变点的影响,我们提出了一种基于核密度估计(KDE)的实时剩余寿命预测方法。首先,使用非参数累积和方法来检测退化过程中的突变点。然后,使用积分均方误差确定影响KDE准确性的样本数量的突然变化。权重系数是根据实时监控中突然变化点之前和之后相对于最小样本数的样本数变化而自适应分配的。该方法考虑了降解过程中的突变状态,并使用了KDE,它不会为降级过程做出模型结构假设或参数估计。最后,使用齿轮磨损退化数据验证了该方法的有效性。
更新日期:2020-09-11
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