CAJ | 학술논문

针对交通检测器故障诊断过程中，噪声掩盖了部分故障信息以及故障信息分布的多尺度性，提出了一种改进的多尺度主元分析（ MSPCA）模型。模型首先将交通检测器数据进行分段处理，再加入改进的小波阈值除噪，对滤除噪声后的小波系数进行主元分析，最后利用二维贡献图完成故障的定位。模型应用于线圈检测器的故障诊断实验，与MSPCA及自适应主元分析相比，该模型减小了误报率和漏报率，准确率更高，抗噪能力更强。
침대교통검측기고장진단과정중，조성엄개료부분고장신식이급고장신식분포적다척도성，제출료일충개진적다척도주원분석（ MSPCA）모형。모형수선장교통검측기수거진행분단처리，재가입개진적소파역치제조，대려제조성후적소파계수진행주원분석，최후이용이유공헌도완성고장적정위。모형응용우선권검측기적고장진단실험，여MSPCA급자괄응주원분석상비，해모형감소료오보솔화루보솔，준학솔경고，항조능력경강。
During the process of data fault information , it is found that the fault information has a property of multi -scale, and sometimes parts of them are covered by random high frequency noise , so a model of data fault diagnosis based on improved multiscale principal component analysis (MSPCA) was presented.Firstly, an improved wavelet threshold method was joined , which was used to remove most of random high frequency noise and then improved the data confidence .Secondly , the model de-composed the reconstructed signals by principal component analysis .After this, the model finished the mission of the fault isola-tion by two-dimensional contribution plots .Finally, a case study of loop detector data fault diagnosis showed that the model had many advantages such as lower fault and failing rate , and stronger anti-noise ability .