摘要
车身在线检测的应用成为越来越普遍的趋势,同时也带来了如何有效应用在线检测的100%测量数据的问题。传统的尺寸偏差诊断方法的数据存在依赖工程经验进行诊断报警,费时费力且准确度低的缺点;提出了用时间序列模式识别和主成分分析(PCA)相结合的方法来进行白车身尺寸偏差诊断,实现误差源模式识别和自动报警诊断,并开发相应工具软件在轿车车身实际生产中得到成功验证。
The application of online detection of auto-body has become increasingly a popular trend;However,how to effectively use measurement data of online detection by 100% is a problem.The traditional diagnostic methods of dimensional deviation depend on the presence of engineering experience to judge,which leads to time consumption and low accuracy.An approach combining the time series pattern recognition and Principal Component Analysis（PCA）is applied to diagnose auto-body dimensional deviation,realize pattern recognition of the error source and automatic alarm diagnosis.Meanwhile the appropriate tools are developed and verified successfully in the actual production of car body.
出处
《机械设计与制造》
北大核心
2012年第1期188-190,共3页
Machinery Design & Manufacture
基金
机械系统与振动国家重点实验室自主课题资助项目(MSV-MS-2010-06)
关键词
时间序列
模式识别
偏差诊断
车身在线检测
Time series
Pattern recognition
Diagnosis of deviation
BIW online detection
