多传感器特征决策融合的钢轨裂纹识别方法

随着高速铁路的快速发展,钢轨裂纹的有效检测对于铁路安全运行具有重要的意义.针对基于漏磁信号的钢轨裂纹识别问题,采用多传感器特征决策融合技术,在对漏磁信号进行了时域和频域的多特征提取与融合的基础上,同时对多传感器信号进行决策融合,设计了一种基于支持向量机(SVM)的多传感器信息融合分类器.利用人工裂纹的实测漏磁信号进行实验,相比于提取单一特征和利用单一传感器信号进行识别,提出的方法取得了更好裂纹识别效果,平均识别率达到98％.

Rail crack recognition based on Multi sensor Feature decision Fusion

With the rapid development of high-speed railway,the effective detection of rail cracks for the safe operation of the railway is of great significance.Focused on the problem of rail crack recognition based on magnetic flux leakage signal,multi-sensor feature-decision fusion technology is adopted,which does the multi-sensor signal decision-fusion at the same time of the multi-feature extraction and fusion of the magnetic flux leakage signal in time domain and frequency domain.And a multi-sensor information fusion classifier is designed based on SVM.Using the measured magnetic flux leakage signal of artificial crack,compared with the extraction of a single feature and the use of a single sensor signal to identify,the proposed method achieves a better crack recognition effect whose average recognition rate reaches 98％.