用加权最小二乘法评价公共汽车预防维修效果

doi:10.3969/j.issn.1007-7375.2018.03.010

工业工程 ›› 2018, Vol. 21 ›› Issue (3): 82-86.doi: 10.3969/j.issn.1007-7375.2018.03.010

用加权最小二乘法评价公共汽车预防维修效果

蒋仁言, 陈浩, 陈志高

长沙理工大学汽车与机械工程学院, 湖南长沙 410114

收稿日期:2017-10-26 出版日期:2018-06-30 发布日期:2018-06-20
作者简介:蒋仁言(1956-),男,湖南省人,教授,博士,主要研究方向为质量、可靠性与维修
基金资助:
国家自然科学基金资助项目（71771029）

An Evaluation of Preventive Maintenance Quality of Buses Using Weighted Least Squares Method

JIANG Renyan, CHEN Hao, CHEN Zhigao

School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha 410114, China

Received:2017-10-26 Online:2018-06-30 Published:2018-06-20

摘要/Abstract

摘要： 本文采用加权最小二乘法拟合可修系统失效点过程数据到幂律模型以评价预防维修的质量。权函数与正态密度函数成正比，它赋予靠近维修时间的失效数据大的权值，而赋予远离维修时间的失效数据小的权值。分析了一个实际案例，对比了加权与不加权的最小二乘法所得到的结果。结果表明，对于维修质量评价，加权的最小二乘法优于普通的最小二乘法。

关键词: 失效点过程, 加权最小二乘法, 幂律模型, 维修质量

Abstract: The failure point process data of a repairable system is fitted to the power-law model using a weighted least squares method so as to evaluate the quality of preventive maintenance actions. The used weight function is proportional to the normal density function, which assigns a large weight to the observation near the maintenance time and assigns a small weight to the observation that is far away from the maintenance time. For a real-world example, the evaluation results obtained from the weighted least squares method and the ordinary least squares method are compared. The results show that the weighted least squares method is better than the ordinary least squares method for maintenance quality evaluation.

Key words: failure point process, weighted least squares method, power law model, maintenance quality evaluation

中图分类号:

TB114.3

蒋仁言, 陈浩, 陈志高. 用加权最小二乘法评价公共汽车预防维修效果[J]. 工业工程, 2018, 21(3): 82-86.

JIANG Renyan, CHEN Hao, CHEN Zhigao. An Evaluation of Preventive Maintenance Quality of Buses Using Weighted Least Squares Method[J]. Industrial Engineering Journal , 2018, 21(3): 82-86.

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用加权最小二乘法评价公共汽车预防维修效果

An Evaluation of Preventive Maintenance Quality of Buses Using Weighted Least Squares Method

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