摘要
提出维纳过程与粒子滤波相结合的滚动轴承剩余寿命预测方法,将维纳过程引入粒子滤波状态空间模型,充分利用其随机增量性质,增强模型的非线性表达能力,提高预测的准确性。提出弱跟踪粒子滤波策略调整维纳过程,解决概率密度分布方差过大的问题。该方法在试验台轴承和风力发电机轴承测试数据中均得到验证,可准确预测轴承剩余寿命。
This paper proposes a method for predicting the remaining life of rolling bearings based on the combination of the Wiener process and the particle filter.The Wiener process is introduced into the particle filter state space model to make full use of its random incremental nature to enhance the nonlinear expression ability of the model and improve the accuracy of prediction.Weak-tracking particle filter strategy is used to adjust the Wiener process to solve the problem of too large of probability density distribution.The method in this paper has been verified by the test data of the bearing of the test bench and the wind turbine bearing on-site,accurately predicting the remaining life of the bearing.
作者
丁显
徐进
黎曦琳
滕伟
宫永立
Ding Xian;Xu Jin;Li Xilin;Teng Wei;Gong Yongli(China Green Development Investment Group Co.,Ltd.,Beijing 100020,China;Duchengweiye Group Co.,Ltd.,Beijing 100020,China;Key Laboratory of Power Station Energy Transfer Conversion and System(North China Electric Power University),Ministry of Education,Beijing 102206,China)
出处
《太阳能学报》
EI
CAS
CSCD
北大核心
2022年第12期248-255,共8页
Acta Energiae Solaris Sinica
基金
国家自然科学基金(51775186)。
关键词
风电机组
维纳过程
粒子滤波
剩余寿命
轴承性能退化
wind turbines
Wiener process
particle filter
remaining life
bearing performance degradation
