Quantitative detection of vertical track irregularities under non-stationary conditions with variable vehicle speed
Track irregularities directly affect the quality and safety of railway vehicle operations. Quantitative detection and real-time monitoring of track irregularities are of great importance. However, due to the frequent variable vehicle speed, vehicle operation is a typical non-stationary process. The...
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sg-ntu-dr.10356-1805992024-10-19T16:48:57Z Quantitative detection of vertical track irregularities under non-stationary conditions with variable vehicle speed Wang, Qiushi Zhao, Hui Gong, Dao Zhou, Jinsong Xiao, Zhongmin School of Mechanical and Aerospace Engineering Engineering Railway vehicle Track irregularity Track irregularities directly affect the quality and safety of railway vehicle operations. Quantitative detection and real-time monitoring of track irregularities are of great importance. However, due to the frequent variable vehicle speed, vehicle operation is a typical non-stationary process. The traditional signal analysis methods are unsuitable for non-stationary processes, making the quantitative detection of the wavelength and amplitude of track irregularities difficult. To solve the above problems, this paper proposes a quantitative detection method of track irregularities under non-stationary conditions with variable vehicle speed by order tracking analysis for the first time. Firstly, a simplified wheel–rail dynamic model is established to derive the quantitative relationship between the axle-box vertical vibration and the track vertical irregularities. Secondly, the Simpson double integration method is proposed to calculate the axle-box vertical displacement based on the axle-box vertical acceleration, and the process error is optimized. Thirdly, based on the order tracking analysis theory, the angular domain resampling is performed on the axle-box vertical displacement time-domain signal in combination with the wheel rotation speed signals, and the quantitative detection of the track irregularities is achieved. Finally, the proposed method is validated based on simulation and field test analysis cases. We provide theoretical support and method reference for the quantitative detection method of track irregularities. Published version This work is supported by the National Natural Science Foundation of China (Grant No. 51805373). We thank the China Scholarship Council for supporting Qiushi Wang to study at the School of Mechanical and Aerospace Engineering at Nanyang Technological University, Singapore (the support number is 202106260138). 2024-10-14T06:51:28Z 2024-10-14T06:51:28Z 2024 Journal Article Wang, Q., Zhao, H., Gong, D., Zhou, J. & Xiao, Z. (2024). Quantitative detection of vertical track irregularities under non-stationary conditions with variable vehicle speed. Sensors, 24(12), 3804-. https://dx.doi.org/10.3390/s24123804 1424-8220 https://hdl.handle.net/10356/180599 10.3390/s24123804 24 2-s2.0-85197160908 12 24 3804 en Sensors © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). application/pdf |
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Engineering Railway vehicle Track irregularity Wang, Qiushi Zhao, Hui Gong, Dao Zhou, Jinsong Xiao, Zhongmin Quantitative detection of vertical track irregularities under non-stationary conditions with variable vehicle speed |
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Track irregularities directly affect the quality and safety of railway vehicle operations. Quantitative detection and real-time monitoring of track irregularities are of great importance. However, due to the frequent variable vehicle speed, vehicle operation is a typical non-stationary process. The traditional signal analysis methods are unsuitable for non-stationary processes, making the quantitative detection of the wavelength and amplitude of track irregularities difficult. To solve the above problems, this paper proposes a quantitative detection method of track irregularities under non-stationary conditions with variable vehicle speed by order tracking analysis for the first time. Firstly, a simplified wheel–rail dynamic model is established to derive the quantitative relationship between the axle-box vertical vibration and the track vertical irregularities. Secondly, the Simpson double integration method is proposed to calculate the axle-box vertical displacement based on the axle-box vertical acceleration, and the process error is optimized. Thirdly, based on the order tracking analysis theory, the angular domain resampling is performed on the axle-box vertical displacement time-domain signal in combination with the wheel rotation speed signals, and the quantitative detection of the track irregularities is achieved. Finally, the proposed method is validated based on simulation and field test analysis cases. We provide theoretical support and method reference for the quantitative detection method of track irregularities. |
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School of Mechanical and Aerospace Engineering |
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School of Mechanical and Aerospace Engineering Wang, Qiushi Zhao, Hui Gong, Dao Zhou, Jinsong Xiao, Zhongmin |
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Article |
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Wang, Qiushi Zhao, Hui Gong, Dao Zhou, Jinsong Xiao, Zhongmin |
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Wang, Qiushi |
title |
Quantitative detection of vertical track irregularities under non-stationary conditions with variable vehicle speed |
title_short |
Quantitative detection of vertical track irregularities under non-stationary conditions with variable vehicle speed |
title_full |
Quantitative detection of vertical track irregularities under non-stationary conditions with variable vehicle speed |
title_fullStr |
Quantitative detection of vertical track irregularities under non-stationary conditions with variable vehicle speed |
title_full_unstemmed |
Quantitative detection of vertical track irregularities under non-stationary conditions with variable vehicle speed |
title_sort |
quantitative detection of vertical track irregularities under non-stationary conditions with variable vehicle speed |
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2024 |
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https://hdl.handle.net/10356/180599 |
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1814777711076311040 |