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M.M. Rahman¹, B. Alkali², A.K. Jain², J.M. Parrilla Gutierrez², C. Mcneil³ and J. Nelson³

¹Research in Computing, Department of Mechanical Engineering, Glasgow Caledonian University, United Kingdom
²Department of Mechanical Engineering, Glasgow Caledonian University, Glasgow, United Kingdom
³Siemens Mobility Limited, Glasgow, United Kingdom

doi:10.4203/ccc.7.8.11

M.M. Rahman, B. Alkali, A.K. Jain, J.M. Parrilla Gutierrez, C. Mcneil, J. Nelson, "Predictive Maintenance Optimisation for CCTV Systems in Electric Multiple Unit Trains Using Machine Learning Techniques", in J. Pombo, (Editor), "Proceedings of the Sixth International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 7, Paper 8.11, 2024, doi:10.4203/ccc.7.8.11

Keywords: CCTV system, rolling stock, machine learning, predictive model, reliability, electric multiple unit.

Abstract

This paper presents the study conducted to investigate the maintenance strategy to improve the reliability of Closed-Circuit Television (CCTV) systems in railway rolling stock Electric Multiple Unit trains. The project attempts to optimise maintenance procedures by assessing 1214 sample datasets collected from sensors and control units during fleet data processing to identify and forecast CCTV faults. The analysis indicates that the CCTV system is the worst-performing system leading to delay and cancellation of service operations. The study attempts to address the pattern of CCTV failures using predictive modelling tools, and machine learning techniques such as Random Forest Regressor, Gradient Boosting Regressor, XGBoost Regressor, and Decision Tree Regressor used for modelling and prediction. The results exhibit satisfactory predictive accuracy of the incident reported days, starting date, and issue date for each incident, the results show important performance indicators such as Mean Squared Error, R-squared and Mean Absolute Error, which indicate promising outcomes. The results emphasise the capability of predictive modelling to improve the dependability of CCTV systems in railway rolling equipment, leading to enhanced operational efficiency and passenger safety.

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Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 7 PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE Edited by: J. Pombo Paper 8.11 Predictive Maintenance Optimisation for CCTV Systems in Electric Multiple Unit Trains Using Machine Learning Techniques M.M. Rahman¹, B. Alkali², A.K. Jain², J.M. Parrilla Gutierrez², C. Mcneil³ and J. Nelson³ ¹Research in Computing, Department of Mechanical Engineering, Glasgow Caledonian University, United Kingdom ²Department of Mechanical Engineering, Glasgow Caledonian University, Glasgow, United Kingdom ³Siemens Mobility Limited, Glasgow, United Kingdom doi:10.4203/ccc.7.8.11 Full Bibliographic Reference for this paper M.M. Rahman, B. Alkali, A.K. Jain, J.M. Parrilla Gutierrez, C. Mcneil, J. Nelson, "Predictive Maintenance Optimisation for CCTV Systems in Electric Multiple Unit Trains Using Machine Learning Techniques", in J. Pombo, (Editor), "Proceedings of the Sixth International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 7, Paper 8.11, 2024, doi:10.4203/ccc.7.8.11 Keywords: CCTV system, rolling stock, machine learning, predictive model, reliability, electric multiple unit. Abstract This paper presents the study conducted to investigate the maintenance strategy to improve the reliability of Closed-Circuit Television (CCTV) systems in railway rolling stock Electric Multiple Unit trains. The project attempts to optimise maintenance procedures by assessing 1214 sample datasets collected from sensors and control units during fleet data processing to identify and forecast CCTV faults. The analysis indicates that the CCTV system is the worst-performing system leading to delay and cancellation of service operations. The study attempts to address the pattern of CCTV failures using predictive modelling tools, and machine learning techniques such as Random Forest Regressor, Gradient Boosting Regressor, XGBoost Regressor, and Decision Tree Regressor used for modelling and prediction. The results exhibit satisfactory predictive accuracy of the incident reported days, starting date, and issue date for each incident, the results show important performance indicators such as Mean Squared Error, R-squared and Mean Absolute Error, which indicate promising outcomes. The results emphasise the capability of predictive modelling to improve the dependability of CCTV systems in railway rolling equipment, leading to enhanced operational efficiency and passenger safety. download the full-text of this paper (PDF, 11 pages, 744 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
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