Bearing life prediction method based on phase space reconstruction of state tracking features

被引：0
|
作者：
Bo L. [1 ]
Yan K. [1 ]
Liu X. [1 ]
机构：
[1] State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing
来源：
Zhendong yu Chongji/Journal of Vibration and Shock | 2019年 / 38卷 / 23期
关键词：
ACPSO; GRNN; Phase space reconstruction; Rolling bearing life prediction; SVDD;
D O I：
10.13465/j.cnki.jvs.2019.23.017
中图分类号：
学科分类号：
摘要：
Aiming at feature selection and model optimization problems in residual life prediction of rolling bearing, a bearing life prediction method based on phase space reconstruction of state tracking feature was proposed. Based on monotonicity and sensitivity assessment of bearing features, quantitative evaluation was done for tracking capability of bearing running state to screen the optimal feature set of bearing performance degradation. In order to uniformly describe each feature's representation information for bearing degradation state, the adaptive chaos particle swarm optimization (ACPSO) algorithm was used to optimize support vector data description (SVDD), and construct the bearing health index. This index was used to accurately divide bearing operation states. Finally, based on the phase space reconstruction index of bearing recession, ACPSO-GRNN was used to predict bearing residual life. Test results showed that the proposed method can be used not only to find the decline time point of bearing operation as soon as possible, but also have higher prediction accuracy than those of SVR and BP neural networks. © 2019, Editorial Office of Journal of Vibration and Shock. All right reserved.
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页码：119 / 125
页数：6
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