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Railway track degradation modelling using dynamic multi-regression to predict failure patterns
Railway tracks degrade and may eventually break down due to several operational and environmental impacts that affect the rails’ reliability. The most common type of railhead defect is called the rail squat, which cost Network Rail an estimate of approximately 3.9 million pounds annually. Squat defects are minor subsurface laminations that run diagonally down the running surface and spread laterally and longitudinally over and along the rail tracks (Li, Zili et al., 2008a; Li, Zili et al., 2008b; Li, Z., 2009). The occurrence of squat defects has a significant impact on the track performance, leading to speed restrictions, delays, and cancellation of in-service train operations and hence penalties for infrastructure owners. To ensure the performance and efficiency of service operations and more reliable railway infrastructure, the UK is investing in railway modernization projects to meet this demand (Rail director April 2023, 2023; MyBib Contributors, 2019). This study uses a Dynamic Multiple linear regression to model the relationship between squat defects and influential parameters such as track length, maximum permissible speed, maximum axle load, estimated million gross tonnage, tamping frequency, rail grinding frequency, and corrugation frequency detection. A hazard model is proposed and used to predict transition probabilities between defect and failure. This research evaluated six years of data acquired from network rail on squats defects, grinding maintenance, and corrugation faults across the UK railway network. The dynamic Markov model is used within the scope of the hazard model to determine the transition probabilities of squat defects propagation. A complete squat data analysis is performed by comparing the efficiency of the rail gridding maintenance regime to the cumulative squat defect frequency against the number of repair operations. An example simulation is shown to anticipate the time to breakdown of railway track systems.
Railway track degradation modelling using dynamic multi-regression to predict failure patterns
Railway tracks degrade and may eventually break down due to several operational and environmental impacts that affect the rails’ reliability. The most common type of railhead defect is called the rail squat, which cost Network Rail an estimate of approximately 3.9 million pounds annually. Squat defects are minor subsurface laminations that run diagonally down the running surface and spread laterally and longitudinally over and along the rail tracks (Li, Zili et al., 2008a; Li, Zili et al., 2008b; Li, Z., 2009). The occurrence of squat defects has a significant impact on the track performance, leading to speed restrictions, delays, and cancellation of in-service train operations and hence penalties for infrastructure owners. To ensure the performance and efficiency of service operations and more reliable railway infrastructure, the UK is investing in railway modernization projects to meet this demand (Rail director April 2023, 2023; MyBib Contributors, 2019). This study uses a Dynamic Multiple linear regression to model the relationship between squat defects and influential parameters such as track length, maximum permissible speed, maximum axle load, estimated million gross tonnage, tamping frequency, rail grinding frequency, and corrugation frequency detection. A hazard model is proposed and used to predict transition probabilities between defect and failure. This research evaluated six years of data acquired from network rail on squats defects, grinding maintenance, and corrugation faults across the UK railway network. The dynamic Markov model is used within the scope of the hazard model to determine the transition probabilities of squat defects propagation. A complete squat data analysis is performed by comparing the efficiency of the rail gridding maintenance regime to the cumulative squat defect frequency against the number of repair operations. An example simulation is shown to anticipate the time to breakdown of railway track systems.
Railway track degradation modelling using dynamic multi-regression to predict failure patterns
Santos, NG.D. (author) / Alkali, B.M. (author) / Kumar, A.J. (author) / Niculita, O. (author)
2023-07-06
Santos , NG D , Alkali , B M , Kumar , A J & Niculita , O 2023 , ' Railway track degradation modelling using dynamic multi-regression to predict failure patterns ' , 12th IMA International Conference on Modelling in Industrial Maintenance and Reliability , Nottingham, UK , United Kingdom , 4/07/23 - 6/07/23 . https://doi.org/10.19124/ima.2023.01.3
Conference paper
Electronic Resource
English
DDC:
624
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