上海交通大学学报

上海交通大学学报（自然版） ›› 2013, Vol. 47 ›› Issue (05): 697-702.

• 机械仪表工程 • 上一篇    下一篇

车身检测数据的多元贝叶斯建模与质量监控

邹景明，金隼，储国平   

  1.  

    (上海交通大学 上海市复杂薄板结构数字化制造重点实验室, 上海  200240）
     
     
  • 收稿日期:2012-09-03 出版日期:2013-05-28 发布日期:2013-05-28
  • 基金资助:

    国家自然科学基金委员会创新研究群体科学基金（51121063），教育部高等学校学科创新引智计划（B06012），国家自然科学基金项目（51175340）资助

Multivariate Empirical Bayes Modeling and Quality Monitoring for Autobody Measuring Data

ZOU Jingming,JIN Sun,CHU Guoping
  

  1. (Shanghai Key Laboratory of Digital Manufacturing for Thin-Walled Structures, Shanghai Jiaotong University, Shanghai 200240, China)
  • Received:2012-09-03 Online:2013-05-28 Published:2013-05-28

摘要：

提出了一种融入动态干预算法的多元经验贝叶斯（MEB）模型，并用于评价车身尺寸的均值、标准差等质量参数.该模型采用较为平稳的历史测量数据和相关性较强的多元测点信息进行误差修正，显著减小了由系统误差而导致的MEB模型的评价误差.构造了一组基于模式识别的动态干预算法、用于自动识别制造过程中的系统误差模式.同时，以某车型车身测量数据处理为例，验证了所提出方法的有效性.

 
 

关键词: 多元小样本, 经验贝叶斯, 动态干预, 车身尺寸, 检测

Abstract:

A multivariate empirical Bayesian (MEB) model with a dynamic intervention algorithm was developed to evaluate mean value, standard deviation and other quality parameters. This model takes full advantage of historical data which is smooth in characteristic and also information from other relevant points in error correction. This algorithm can remarkably reduce MEB error in the evaluation and simultaneously a series of error modes in manufacturing process are detected and formulated. Based on recognition of these error patterns, a dynamic intervention algorithm was developed. For validation and verification, this method was applied to the data from multipoints of a vehicle body and the result turns out to be satisfactory.
 

Key words: multivariate and small sample, empirical Bayes, dynamic intervention, autobody dimension, measuring

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