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RISS 인기검색어
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- 저자 : Zunsong Ren (검색결과 1 건)
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Xin Xin,Zunsong Ren,Yi Yin,Jinsheng Gao 대한기계학회 2022 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.36 No.12
Mortar void is a hidden defect in ballastless slab track difficult to be efficiently identified by traditional detection methods. This paper is dedicated to proposing a new detection method to identify the mortar void position and length using the vehicle response combined with the hybrid convolutional neural network-support vector machine (CNN-SVM) classifier. The vertical wheelset accelerations with different mortar void conditions are collected from a vehicle-track coupled dynamics simulation model. The first components decomposed from wheelset accelerations by local mean decomposition and their envelopes are utilized as the training data due to their sensitivity to mortar void. To improve the identification precision, the scope descent method is proposed to determine the range influenced by mortar void (IMVR) and samples are labeled according to IMVR. Meanwhile, identification results are post processed based on the mortar void characteristics. The results show that over 90 % mortar void conditions with the length of 0.65 m are detected correctly and the identification has a higher precision with the mortar void length greater than 0.95 m. The proposed technology of mortar void detection using the wheelset accelerations with the hybrid CNN-SVM classifier provides reference for engineering application, which is of great significance to relieve the pressure of health monitoring of railway track.
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