改进的主成分分析方法在磁浮系统中的应用  

An Improved Principle Components Analysis Method and Its Application to Maglev System

作　　者：邹东升[1] 佘龙华[1] 

机构地区：[1]国防科技大学机电工程与自动化学院,长沙410073

出　　处：《振动．测试与诊断》2009年第1期96-100,共5页Journal of Vibration,Measurement & Diagnosis

摘　　要：为解决磁悬浮列车中多传感器系统的故障诊断和信号重构,提出了一种改进的主成分分析方法。该算法能够改进传统主成分分析方法在有相位偏移信号的系统中处理效果不佳的缺陷。算法理论证明过程也给出了实施改进方法的具体步骤。最后,在磁浮列车传感器信号系统中,基于改进前后的两种主成分分析方法,采用平方预报误差原则,分传感器有故障和无故障两种情况,把故障的检测结果进行了对比。算例结果表明,改进后的算法能明显提高故障的检测概率,降低检测误差。An improved principle components analysis method(PCA) was proposed for fault diagnosis and faulty signal reconstruction in a maglev train,which had multiple sensors and redundant signals.In systems with phase shifting signals,the main components acquired by the traditional PCA method were not optimal,so the processing effects were not convincing.To overcome this flaw,an improved idea was put forward and followed by a strict demonstration in theory.What's more,the practical realization process of the idea was also acquired in the demonstration process.Finally,adopting the squared prediction error principle,the traditional and improved PCA methods were both used in the maglev system fault detection.Whether the system has faulty signals or not,the conclusion is that the improved PCA method can increase the probability of faulty sensor identification and decrease the error.

关 键 词：主成分分析 故障诊断 平方预报误差 磁浮系统 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]