车身检测数据的多元贝叶斯建模与质量监控

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.

Multivariate Empirical Bayes Modeling and Quality Monitoring for Autobody Measuring Data

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.