基于主元分析的桥梁挠度传感器故障诊断研究  

Bridge deflection sensor fault diagnosis research based on PCA

作　　者：胡顺仁[1,2] 李瑞平[1] 包明[1] 张建科[1] 

机构地区：[1]重庆理工大学电子信息与自动化学院,重庆400054 [2]重庆大学光电技术及系统教育部重点实验室,重庆400044

出　　处：《传感器与微系统》2014年第6期9-12,共4页Transducer and Microsystem Technologies

基　　金：国家"863"计划资助项目(2006AA04Z433);重庆市科技攻关重大专项资助项目(CYB-DQ-0004);重庆市科委科技攻关资助项目(2010GGD004)

摘　　要：主元分析(PCA)是一种典型的数据降维的多元统计方法,已被越来越多地用于故障诊断。将PCA应用在桥梁挠度传感器故障诊断。介绍了PCA的理论,研究了基于PCA的故障检测方法和基于贡献率的故障诊断方法。计算平方预测误差(SPE)和Hoteling T2统计,当统计量超过阈值时,判断系统出现了传感器故障,然后通过SPE贡献图判断故障源。通过仿真验证了PCA在故障诊断的实用性,但结果也表明:PCA对小故障不是很敏感。Principal component analysis （PCA）is a typical data dimension reduction multivariate statistical method, which has been used in fault diagnosis. PCA is applied in bridge deflection sensor fault diagnosis. Introduce theory of PCA, and study fault detection method based on PCA and fault diagnosis method based on contribution rate. Calculate squared prediction error （SPE） and Hotelling T^Q statistics, when statistics exceeds threshold,judge there are sensor faults in system, and by using SPE contribution figure judge fault source. By simulation, the practicability of PCA in fault diagnosis is proved, but the result also show that PCA is not so sensitive to small fault.

关 键 词：桥梁监测 平方预报误差 主元分析 故障诊断 

分 类 号：U446.2[建筑科学—桥梁与隧道工程] TP274[交通运输工程—道路与铁道工程]