摘要
为弥补现有补偿电容检测方法在检测及时性和检测成本等方面的不足,基于传输线理论,提出了机车信号感应电压幅值包络(IVECS)仿真模型;在此基础上,分析了补偿电容故障对IVECS的影响规律,建立并验证了基于频率的IVECS等效回归模型;利用补偿电容故障位置前后IVECS的频率变化,提出了基于自适应最优核时-频分布的补偿电容故障诊断方法.该方法基于机车信号的实际数据,采用B样条离散二进小波对数据进行滤波降噪处理,并利用自适应最优核时-频分布提取处理结果的时-频信息,再通过检测时-频信息中频率的变化实现对故障电容的定位.经过对随机抽取的100段实际数据的测试,准确率约为92.58%,表明该方法能准确检测轨道电路发生故障的补偿电容位置.
In order to make up for the shortcoming of the existing methods for compensation capacitor fault detection in both timeliness and cost aspects,a simulation model for induced voltage envelope of cab signal was proposed based on the transmission-line theory.On this basis,the influence law of compensation capacitor failure on the induced voltage envelope was analyzed,and a frequency-based equivalent regression model for the induced voltage envelope was built and verified.Based on this regression model,a compensation capacitor fault detection method was proposed based on the AOK-TFR（adaptive optimal kernel time-frequency representation） and the fact that the frequency of the induced voltage envelope changes ahead and behind the location of the defected capacitor.In this method,the signal to be processed,directly from a cab signal recorder,is denoised by using the B-spline discrete binary wavelet transform（DBWT）.Then its time-frequency distribution information is extracted by the AOK-TFR,and the fault compensation capacitor is lastly located in light of the change of the frequency extracted.The experimental result of actual data from 100 segments selected randomly shows that the accuracy rate is about 92.58% to indicate that the proposed method can accurately detect the location of a defected compensation capacitor under the condition that other capacitors are in good condition.
出处
《西南交通大学学报》
EI
CSCD
北大核心
2011年第1期84-91,共8页
Journal of Southwest Jiaotong University
基金
国家863计划资助项目(0912JJ0104-XH00-H-HZ-001-20100419)
关键词
故障诊断
无绝缘轨道电路
补偿电容
自适应最优核时-频分布
B样条离散二进小波变换
fault diagnosis
jointless track circuit
compensation capacitor
adaptive optimal kernel time-frequency representation
B-spline discrete binary wavelet transform
