Remaining Useful Life Prediction of Rolling Bearings Based on Segmented Relative Phase Space Warping and Particle Filter
Remaining Useful Life Prediction of Rolling Bearings Based on Segmented Relative Phase Space Warping and Particle Filter
复制标题
基于分段相对相空间扭曲和粒子滤波的滚动轴承剩余寿命预测
DOI：
10.1109/tim.2022.3214623
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发表时间：
2022-01-01
影响因子：
5.6
通讯作者：
Song, Gangbing
中科院分区：
文献类型：
--
作者：
Liu, Hengyu;Yuan, Rui;Song, Gangbing
预测性维修在智能机械故障诊断领域起着至关重要的作用，提高了维修效率。本文主要研究滚动轴承预测维修中实时损伤特征的提取和剩余使用寿命的预测。一些规则学习预测方法缺乏动态基础，需要大量的数据和先验知识。本文提出了分段相对相空间翘曲（SRPSW）算法和双指数模型（DEM）与粒子滤波（PF）相结合的RUL预测策略。SRPSW为不同阶段的RUL实时预测提供了动态依据。基于dem的PF减少了对先验知识的需求，提高了精度。正常和加速退化实验的分析结果表明，提出的SRPSW克服了原始PSW在描述轴承后期运行阶段的失败。此外，SRPSW提取的相对损伤指标比常用指标更准确。预测结果表明，基于dem的预测模型在保证预测精度的同时，不需要专业和先验信息。本文提出的方法为轴承的预测性维护提供了一条新的途径。
Predictive maintenance plays a crucial role in the field of intelligent machinery fault diagnosis, which improves the efficiency of maintenance. This article focuses on the extraction of real-time damage feature and the prediction of remaining useful life (RUL) in predictive maintenance of rolling bearings. Some RUL prediction approaches lack dynamic foundations and require large amounts of data and prior knowledge. This article proposes the algorithm of segmented relative phase space warping (SRPSW) and a strategy combining double exponential model (DEM) and particle filter (PF) to predict the RUL. SRPSW provides a dynamic basis for real-time RUL prediction in different stages. The DEM-based PF reduces the need for prior knowledge and improves the accuracy. The analysis results from normal and accelerated degradation experiments show that the proposed SRPSW overcomes the failure of the original PSW in depicting the later operating stage of bearings. Further, the relative damage indicators (RDIs) extracted by SRPSW are more accurate than commonly used indicators. The predicted results show that the DEM-based PF does not require professional and prior information while ensuring the accuracy of RUL prediction. The proposed approach in this article provides a new avenue for predictive maintenance of bearings.