摘要
为了对轴承振动性能序列进行动态预报,将自助法与最小二乘法进行有效融合,提出了一种基于自助最小二乘线性拟合的轴承振动性能序列动态预报模型。首先,采用自助法对紧邻的10个振动数据进行了模拟抽样,构造出了当前状态下多组振动侧面信息,将每组轴承振动信息利用自助最小二乘法进行了线性拟合;然后,运用最大熵原理获得了拟合系数a和c的概率密度函数、真值及估计区间,进而得到了滚动轴承振动时间序列的真值拟合与区间拟合;通过不断更新紧邻的10个振动数据,实现了滚动轴承振动性能真值与区间的动态预报;最后,采用某轴承3个服役时间段的振动性能案例,对轴承振动性能序列动态预报模型的准确性进行了验证。研究结果表明:采用预报模型获得的预报值与实际值可保持良好的一致性,其最大预报误差仅为14.73%,同时预报区间差值小、精度高;该振动性能序列动态预报模型可较好地对应用于工程实际中的轴承进行健康监测及安全诊断。
In order to dynamically predict the bearing vibration performance sequence,the bootstrap method and the least squares method were effectively integrated,and a dynamic prediction model of the bearing vibration performance sequence based on the bootstrap-least squares linear fitting was proposed.Firstly,10 adjacent vibration data were simulated sampling by using the bootstrap method to form multiple sets of vibration side information at the current state.Then the multiple sets of vibration information were linear fitted by using the least squares method.Secondly,the probability density function,truth value and estimated interval of fitting coefficients a and c were obtained by the bootstrap-maximum entropy principle,and the truth value fitting and the interval fitting of rolling bearing vibration time series could be obtained.The dynamic prediction of the true value and interval of the vibration performance of rolling bearings were realized by constantly updating the 10 adjacent vibration data.Finally,the accuracy of the dynamic prediction model of the bearing vibration performance sequence was verified by using the vibration performance cases of a bearing in three service periods.The results indicate that the prediction value obtained using forecast models is in good agreement with the actual value,and the maximum prediction error is only 14.73%.The prediction interval difference is small and the accuracy is high.The dynamic prediction model of vibration performance sequence can better perform health monitoring and safety diagnosis for bearings applied in engineering practice.
作者
邵立东
常振
陆水根
曹茂来
SHAO Li-dong;CHANG Zhen;LU Shui-gen;CAO Mao-lai(School of Geely Automobile,Hangzhou Vocational&Technical College,Hangzhou 310018,China;Hangzhou Bearing Test&Research Center Co.,Ltd.,Hangzhou 310022,China;Machinery Industry Bearing Quality Inspection Center,Hangzhou 310022,China)
出处
《机电工程》
CAS
北大核心
2022年第6期791-798,共8页
Journal of Mechanical & Electrical Engineering
基金
国家重点研发计划资助项目(2020YFB2006803)
宁波市“科技创新2025”重大专项资助项目(2018B10005)。
关键词
滚动轴承性能退化
振动信号
动态预报模型
自助最小二乘法
概率密度函数
预报区间差
轴承健康监测
轴承早期失效
rolling bearing performance degradation
vibration signals
dynamic prediction model
self-help least square method
probability density function
prediction interval difference
bearing health monitoring
bearing early failure