摘要
为提高拖拉机的可靠性,减少维修次数,运用可靠性分析理论对其使用寿命进行分析研究。首先对某型号轮式拖拉机进行750 h的现场使用跟踪试验,将收集到的故障时间数据分析整理,采用二参数威布尔分布对其进行建模,然后利用四分段距法筛选有效数据,使用最小二乘法和极大似然估计法进行模型参数求解,选取最优的参数求解方法,最后采用K-S检验法对威布尔分布模型进行检验。结果表明,拖拉机的故障时间服从二参数威布尔分布。通过模型计算得到拖拉机的平均无故障工作时间为317.78 h,中位寿命为451.28 h,特征寿命为513.45 h,随后以A、B、C、D四台拖拉机为例,进行剩余寿命预测,得到使用时间和剩余寿命之间具体的对应关系,从而对拖拉机制定合理的预防性维修周期。
In order to improve the reliability of the tractor and reduce the maintenance times,the service life of the tractor is analyzed and researched using the reliability analysis theory.Firstly,a 750 h field usage tracking experiment of a certain type of wheeled tractor is carried out,the collected failure time data is analyzed and sorted,and the two-parameter Weibull distribution is used to model it.Then,using inter-quartile range method to screen effective data,the least squares method and the maximum likelihood estimation method are used to solve the model parameters,and the optimal parameter solving method is selected.Finally,the K-S test method is used to test the Weibull distribution model.The results show that the failure time of the tractor obeys a two-parameter Weibull distribution.Through the model calculation,the average trouble-free working time of the tractor is 317.78 h,the median life is 451.28 h,and the characteristic life is 513.45 h.Then,taking the four tractors A,B,C,and D as examples,the remaining life prediction is carried out,and the specific corresponding relationship between the service time and the remaining life is obtained,so as to formulate a reasonable preventive maintenance cycle for the tractors.
作者
文昌俊
邵明颖
何永豪
陈凡
陈洋洋
Wen Changjun;Shao Mingying;He Yonghao;Chen Fan;Chen Yangyang(School of Mechanical Engineering,Hubei University of Technology,Wuhan,430068,China;Hubei Agricultural Machinery Appraisal Station,Wuhan,430068,China)
出处
《中国农机化学报》
北大核心
2023年第11期73-78,共6页
Journal of Chinese Agricultural Mechanization
基金
湖北省重点研发计划项目(2020BBB063)。
关键词
轮式拖拉机
可靠性
威布尔分布
极大似然估计
K-S检验
剩余寿命预测
wheeled tractor
reliability
Weibull distribution
maximum likelihood estimation
K-S test
remaining life prediction
