摘要
为了对转向架关键部件进行及时的性能检测和故障诊断,实验选用高速列车转向架典型故障振动信号,先进行小波分解,在各个子频带上提取小波熵特征,用于反映振动信号在各尺度上的复杂程度。在多个小波熵特征张成的高维特征空间中对四种转向架典型故障工况进行支持向量机分类识别,实验结果表明识别率随运行速度逐步提高,在速度达到200 km/h时得到了90%以上的识别率,验证了小波熵特征对于高速列车故障信号分析的有效性。
Performance monitoring and fault diagnosis for the critical component of bogie is very important. Simulation data of high speed train bogie fault signal was selected in the data experiment. Based on multiresolution analysis, wavelet entropies were extracted to reflect the complexity level of the vibration signal on scales. In the high dimension composed by several wavelet entropy features, the dates from four fault patterns were classified and recognition rate is above 90% when the speed over 200 km/h. The wavelet entropy feature is effective for fault signal analysis of high speed train bogie.
出处
《计算机应用研究》
CSCD
北大核心
2013年第12期3657-3659,3663,共4页
Application Research of Computers
基金
国家自然科学基金重点资助项目(61134002)
国家自然科学基金资助项目(61075104)
中央高校基本科研业务费专项资金资助项目(SWJTU11BR039
SWJTU11ZT06)
关键词
故障诊断
特征提取
转向架
小波熵
支持向量机
fault diagnosis
feature extraction
bogie
wavelet entropy
support vector machine（SVM）
