Heavy-haul railways play a vital role in freight transportation,and the health of the rails directly impacts the safety and efficiency of railway operations.The heavy axle loads and long train compositions of heavy-ha...Heavy-haul railways play a vital role in freight transportation,and the health of the rails directly impacts the safety and efficiency of railway operations.The heavy axle loads and long train compositions of heavy-haul trains make the rail surface susceptible to damage such as rail corrugation,spalling and abrasion,threatening operational safety.To address the issue,this paper proposes a multi-source data fusion method for identifying rail surface defects on heavy-haul railways.First,complete ensemble empirical mode decomposition with adaptive noise is used to decompose vibration signals and extract multi-dimensional vibration features.Next,dynamic time warping is applied to align rail profile data and extract key geometric features.Then,the vibration features and profile features are fused using Relief-F to select the most discriminative features.Finally,a support vector machine is utilized for defect identification.Experiment results show that the proposed method achieves high accuracy in identifying rail surface defects,with an accuracy of 96.4%.展开更多
基金supported by the National Key R&D Program of China(No.2021YFF0501102)the National Natural Science Foundation of China(Grants No.52202392,U2368202,52372308,U2468203 and U2468206).
文摘Heavy-haul railways play a vital role in freight transportation,and the health of the rails directly impacts the safety and efficiency of railway operations.The heavy axle loads and long train compositions of heavy-haul trains make the rail surface susceptible to damage such as rail corrugation,spalling and abrasion,threatening operational safety.To address the issue,this paper proposes a multi-source data fusion method for identifying rail surface defects on heavy-haul railways.First,complete ensemble empirical mode decomposition with adaptive noise is used to decompose vibration signals and extract multi-dimensional vibration features.Next,dynamic time warping is applied to align rail profile data and extract key geometric features.Then,the vibration features and profile features are fused using Relief-F to select the most discriminative features.Finally,a support vector machine is utilized for defect identification.Experiment results show that the proposed method achieves high accuracy in identifying rail surface defects,with an accuracy of 96.4%.
