Position synchronization for track geometry inspection data via big-data fusion and incremental learning |
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| Institution: | 1. School of Civil Engineering, Southwest Jiaotong University, Chengdu, China;2. Key Laboratory of High-speed Railway Engineering, Ministry of Education, Chengdu, China;3. Department of Civil and Environmental Engineering, Rutgers, The State University of New Jersey, NJ, USA;1. Key Laboratory of High-speed Railway Engineering in Ministry of Education, School of Civil Engineering, Southwest Jiaotong University, Chengdu, China 610031;2. Department of Industrial and Systems Engineering, University at Buffalo, The State University of New York, 314 Bell Hall, Buffalo, NY 14260, USA;3. Department of Civil, Structural and Environmental Engineering, University at Buffalo, The State University of New York, 212 Ketter Hall, Buffalo, NY 14260, USA;1. College of Engineering and Technology, Southwest University, Chongqing 400716, China;2. National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology, Chongqing 400716, China;3. Research School of Engineering, College of Engineering and Computer Science, The Australian National University, Canberra, Australian Capital Territory 2601, Australia;4. School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China;5. Key Laboratory of High-speed Railway Engineering, Ministry of Education, Chengdu 610031, China;6. School of System Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen 518055, China;7. Siemens Industrial Automation Products Ltd., Chengdu 611731, China;1. P6600, 6/F, Yeung Kin Man Academic Building, Department of Systems Engineering and Engineering Management, City University of Hong Kong, Hong Kong;2. School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing 100083, China;3. Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China;1. School of Civil Engineering, Southwest Jiaotong University, Chengdu, China;2. Key Laboratory of High-Speed Railway Engineering, Ministry of Education, Chengdu, China;3. School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China;4. Department of Civil and Environmental Engineering, Rutgers, The State University of New Jersey, NJ, USA |
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| Abstract: | Track geometry inspection data is important for managing railway infrastructure integrity and operational safety. In order to use track geometry inspection data, having accurate and reliable position information is a prerequisite. Due to various issues identified in this research, the positions of different track geometry inspections need to be aligned and synchronized to the same location before being used for track degradation modeling and maintenance planning. This is referred to as “position synchronization”, a long-standing important research problem in the area of track data analytics. With the aim of advancing the state of the art in research on this subject, we propose a novel approach to more accurately and expediently synchronize track geometry inspection positions via big-data fusion and incremental learning algorithms. Distinguishing it from other relevant studies in the literature, our proposed approach can simultaneously address data exceptions, channel offsets and local position offsets between any two inspections. To solve the Position Synchronization Model (PS-Model), an Incremental Learning Algorithm (IL-Algorithm) is developed to handle the “lack of memory” challenge for the fast computation of massive data. A case study is developed based on a dataset with data size of 18 GB, including 58 inspections between February 2014 and July 2016 over 323 km (200 miles) of tracks belonging to China High Speed Railways. The results show that our proposed model performs robustly against data exceptions via the use of multi-channel information fusion. Also, the position synchronization error using our proposed approach is within 0.15 meters (0.5 feet). Our proposed data-driven, incremental learning algorithm can quickly solve the complex, data-extensive, position synchronization problem, using an average of 0.1 s for processing one additional kilometer of track. In general, the data analysis methodology and algorithm presented in this paper are also suitable to address other relevant position synchronization problems in transportation engineering, especially when the dataset contains multiple channels of sensors and abnormal data outliers. |
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| Keywords: | Track geometry inspection Railway Big data Information fusion Position synchronization Incremental learning |
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