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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 賴勇成 | zh_TW |
| dc.contributor.advisor | Yung-Cheng Lai | en |
| dc.contributor.author | 張騰瀠 | zh_TW |
| dc.contributor.author | Teng-Ying Chang | en |
| dc.date.accessioned | 2023-08-16T16:21:04Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-08-16 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-08-08 | - |
| dc.identifier.citation | A.K.S. Jardine, D. Lin, D. Banjevic A review on machinery diagnostics and prognostics implementing condition-based maintenance Mechanical System and Signal Process, 20 (2006), pp. 1483-1510
Ahmad, R., & Kamaruddin, S. (2012). An overview of time-based and condition-based maintenance in industrial application. Computers & industrial engineering, 63(1), 135-149. Andrés, J., Cadarso, L., & Marín, Á. (2015). Maintenance scheduling in rolling stock circulations in rapid transit networks. Transportation Research Procedia, 10, 524-533. Appoh, F., Yunusa-Kaltungo, A., Sinha, J. K., & Kidd, M. (2021, June). Semi-autonomous framework for propulsion subsystem overhaul and refurbishment. In 2021 International Conference on Artificial Intelligence and Computer Science Technology (ICAICST) (pp. 205-210). IEEE. Bukhsh, Z. A., Saeed, A., Stipanovic, I., & Doree, A. G. (2019). Predictive maintenance using tree-based classification techniques: A case of railway switches. Transportation Research Part C: Emerging Technologies, 101, 35-54. Campbell, S., & Naeem, W. (2012). A rule-based heuristic method for COLREGS-compliant collision avoidance for an unmanned surface vehicle. IFAC proceedings volumes, 45(27), 386-391. Campos, J. (2009). Development in the application of ICT in condition monitoring and maintenance. Computers in industry, 60(1), 1-20. Ciani, L., Guidi, G., Patrizi, G., & Galar, D. (2021). Condition-based maintenance of HVAC on a high-speed train for fault detection. Electronics, 10(12), 1418. Dimitrova, E., & Tomov, S. (2021, September). Digital twins: an advanced technology for railways maintenance transformation. In 2021 13th Electrical Engineering Faculty Conference (BulEF) (pp. 1-5). IEEE. Ebeling, C.E. (1997), An Introduction to Reliability and Maintainability Engineering, McGraw-Hill Inc., New York Gao, Y., Feng, Y., Zhang, Z., & Tan, J. (2015). An optimal dynamic interval preventive maintenance scheduling for series systems. Reliability Engineering & System Safety, 142, 19-30. Genschel, U., & Meeker, W. Q. (2010). A comparison of maximum likelihood and median-rank regression for Weibull estimation. Quality engineering, 22(4), 236-255. Guo, J., Qi, J., & Li, X. (2019). Reliability analysis of emus braking systems with fuzzy dynamic fault tree. China Mechanical Engineering, 30(13), 1585. Herr, N., Nicod, J., Varnier, C., Zerhouni, N., Cherif, M., & Fnaiech, F. (2017, April). Joint optimization of train assignment and predictive maintenance scheduling. In International Conference on Railway Operations Modelling and Analysis. Jardine, A. K., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical systems and signal processing, 20(7), 1483-1510. Jarillo, J. M., Moreno, J., Alfi, S., Barcet, S., Bouvet, P., Bruni, S., ... & Licciardello, R. (2021). Novel technology concepts and architecture for on-board condition-based monitoring of railway running gear: The RUN2Rail vision. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 235(5), 616-630. Kyaw, M. A., Sidorenko, V. G., Buchirin, V. G., & Safronov, A. I. (2019, January). The application of genetic algorithms for the scheduling of electric rolling stock maintenance. In 2019 IEEE Conference of Russian young researchers in electrical and electronic engineering (EIConRus) (pp. 2148-2151). IEEE. Lai, Y. C., Chien, S. C., & Lu, C. L. (2017). Optimal Maintenance Task Generation and Assignment for Rail Infrastructure. Paper presented at the Proceedings of 96th Transportation Research Board, Washington, DC. Lai, Y. C., Fan, D. C., & Huang, K. L. (2015). Optimizing rolling stock assignment and maintenance plan for passenger railway operations. Computers & Industrial Engineering, 85, 284-295. Lai, Y. C., Wang, S. W., & Huang, K. L. (2016). Optimized train-set rostering plan for taiwan high-speed rail. IEEE Transactions on Automation Science and Engineering, 14(1), 286-298. Lai, Y. C., Xu, R. H., & Yan, T. H. (2018). A Rolling-Horizon Optimization Approach for Catenary Maintenance Identification and Assignment. Paper presented at the Proceedings of 97th Transportation Research Board, Washington, DC. Lai, Y. C., Zeng, W. W. and Liu, K. C. (2011). Development of an Operational Rolling Stock Assignment Model for Taiwan High Speed Rail. Proceedings of 9th World Congress on Railway Research. 2011, Lille, France. Li, H., Parikh, D., He, Q., Qian, B., Li, Z., Fang, D., & Hampapur, A. (2014). Improving rail network velocity: A machine learning approach to predictive maintenance. Transportation Research Part C: Emerging Technologies, 45, 17-26. Lin, B., & Zhao, Y. (2021). Synchronized optimization of EMU train assignment and second-level preventive maintenance scheduling. Reliability Engineering & System Safety, 215, 107893. Lusby, R. M., Haahr, J. T., Larsen, J., & Pisinger, D. (2017). A branch-and-price algorithm for railway rolling stock rescheduling. Transportation Research Part B: Methodological, 99, 228-250. Nielsen, L. K., Kroon, L., & Maróti, G. (2012). A rolling horizon approach for disruption management of railway rolling stock. European Journal of Operational Research, 220(2), 496-509. Pham, H. (2003). Handbook of reliability engineering. London: Springer. Pham, H. (2006). Handbook of reliability engineering: Springer Science & Business Media. Rudek, I., & Rudek, R. (2021, August). Heuristic workload scheduling algorithms for rolling stocks with mixed preventive maintenance policies. In 2021 25th International Conference on Methods and Models in Automation and Robotics (MMAR) (pp. 297-302). IEEE. Wu D., Jennings C., Terpenny J., Kumara S., (2016) Cloud-based machine learning for predictive analytics: Tool wear prediction in milling Proceedings - 2016 IEEE international conference on big data, IEEE, pp. 2062-2069 Wu, M. J., & Lai, Y. C. (2019, September). Train-set assignment optimization with predictive maintenance. In RailNorrköping 2019. 8th International Conference on Railway Operations Modelling and Analysis (ICROMA), Norrköping, Sweden, June 17th–20th, 2019 (No. 069, pp. 1131-1139). Linköping University Electronic Press. Wu, M.-J., Lai, Y.-C.R., (2019). Rolling stock assignment and maintenance scheduling with predictive maintenance. Technical Report. Transportation Research Board 98th Annual Meeting. Xie, J., Huang, J., Zeng, C., Jiang, S. H., & Podlich, N. (2020). Systematic literature review on data-driven models for predictive maintenance of railway track: Implications in geotechnical engineering. Geosciences, 10(11), 425. Yin, H., Wang, K., Qin, Y., Hua, Q., & Jiang, Q. (2017). Reliability analysis of subway vehicles based on the data of operational failures. EURASIP Journal on Wireless Communications and Networking, 2017(1), 212 Zhong, Q., Lusby, R. M., Larsen, J., Zhang, Y., & Peng, Q. (2019). Rolling stock scheduling with maintenance requirements at the Chinese High-Speed Railway. Transportation Research Part B: Methodological, 126, 24-44. Ziarati, K., Soumis, F., Desrosiers, J., Gélinas, S., & Saintonge, A. (1997). Locomotive assignment with heterogeneous consists at CN North America. European journal of operational research, 97(2), 281-292. Zonta, T., Da Costa, C. A., da Rosa Righi, R., de Lima, M. J., da Trindade, E. S., & Li, G. P. (2020). Predictive maintenance in the Industry 4.0: A systematic literature review. Computers & Industrial Engineering, 150, 106889. 交通部台灣鐵路管理局（Taiwan Railway Administration, M.O.T.C.）(2016) 臺灣鐵路管理局旅客列車晚點賠償規約 交通部台灣鐵路管理局（Taiwan Railway Administration, M.O.T.C.）(2019) 108年報 交通部台灣鐵路管理局（Taiwan Railway Administration, M.O.T.C.）(2023) 鐵路機車車輛檢修規則 江正文 (Chiang, C.W.). (2011).啟發式演算法之研究與改良.中原大學電子工程學研究所博士論文 吳孟儒 (Wu, M.J.). (2019).考量車組劣化特性之軌道車輛預測性檢修排程. 臺灣大學土木工程學研究所碩士論文 范道中 (Fan, D.C.). (2013).臺鐵車輛調配規劃決策支援模式之研發. 臺灣大學土木工程學研究所碩士論文 凌宇康 (Ling, Y.K.). (2006).應用可靠度為中心維修方式規劃軌道車輛維修階層之研究－以台鐵電聯車動力系統為例. 國立交通大學運輸科技與管理學碩士論文 徐任宏 (Xu, R.H.). (2018).電車線系統年度維護任務產生之決策支援模型研發. 臺灣大學土木工程學研究所碩士論文 陳百傑 (Chen, B.C.). (2002).以啟發式演算法求解時窗限制車輛途程問題.中原大學工業工程學研究所碩士論文 曾微雯 (Zeng, W.W.). (2013).研發台灣高速鐵路列車組指派最佳化模式. 臺灣大學土木工程學研究所碩士論文 簡邵崎 (Chien, S.C.). (2017).軌道系統維護任務產生與指派之決策支援模型研發. 臺灣大學土木工程學研究所碩士論文 顏子皓 (Yen, T.H.). (2018).考量軌道配置下之短期維修排程最佳化. 臺灣大學土木工程學研究所碩士論文 | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88917 | - |
| dc.description.abstract | 鐵路車輛在整體鐵路系統的營運方面扮演著關鍵的角色。一個組織良好的車輛指派和維修計畫可以顯著提高鐵路營運的可靠度、可用度和安全性，從而提高旅客滿意度並降低整體運營成本。在鐵路營運規劃實務中，規劃者會依照已安排之時刻表做車輛調配及檢修排程，以滿足營運及檢修規章需求。在傳統的車輛指派策略中，維修計畫通常基於固定時間間隔或里程進行制定，這可能會導致不必要的檢修或意外故障。基於劣化模式而提出預測性檢修策略，是較有彈性且富有效益之作法，在鐵路車輛領域中，部分鐵路公司已開始嘗試採用預測性檢修策略，然而當前之作法僅考量全車一體適用之劣化特性，檢修排程時則無法納入各子系統有不同的劣化特性之事實。本研究提出了一混合整數規劃模型及一提升求解效率的啟發式演算法，考量鐵路車輛中各子系統（如：如牽引動力系統、轉向架及軔機系統等重要子系統）之檢修排程，以最小化子系統檢修成本、車輛失效所造成的營運損失成本為目標，實現更高效且穩健的車輛調配計畫。由實務案例分析可得知，總成本可以有效的下降12.8%，此成果可支援營運者在兼顧車輛可靠度與檢修成本的情況下，研擬合乎車輛子系統特性之車輛調配與檢修排程計畫。 | zh_TW |
| dc.description.abstract | A well-organized rolling stock assignment and maintenance plan are critical for the reliability, availability, and safety of railway operations. Traditional rolling stock assignments are often based on fixed time intervals or mileage, resulting in unnecessary maintenance or unexpected failures. Predictive maintenance considers the future performance of rolling stock, but current strategies for railway rolling stock only consider the same degradation characteristics for the entire train. This research proposes a comprehensive rolling stock assignment and maintenance plan that considers subsystem maintenance schedules including traction motor, brake control unit and pantograph, minimizing maintenance costs and operational losses due to failure. The performance of subsystems directly affects the effectiveness of the entire system, highlighting their importance in effective maintenance planning. Results from the case study demonstrate that the proposed method can successfully reduce failure cost by 12.8% by adopting a predictive maintenance strategy on a subsystem in a rolling stock assignment. Planners can use the proposed method to determine rolling stock assignments and maintenance plans that effectively balance reliability and costs, thereby enhancing the overall efficiency of the rolling stock. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-16T16:21:04Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-08-16T16:21:04Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 摘要 i
Abstract ii Table of Content iii LIST OF FIGURES v LIST OF TABLES vi CHAPTER 1 INTRODUCTION 1 1.1 Background 1 1.2 Research Objectives 2 1.3 Contribution Summary 4 1.4 Structure of Thesis 5 CHAPTER 2 LITERATURE REVIEW 7 2.1. The Rolling Stock Assignment (RSA) Problem 8 2.2. Maintenance Strategy on rolling stock 9 2.3. The maintenance objectives of rolling stock 13 2.4. Summary of Literature Review 17 CHAPTER 3 Methodology 18 3.1. The Trainset Assignment Process at TRA 19 3.2. Trainset Assignment with Predictive Maintenance Strategies 26 3.2.1 Applying PdM Strategy on subsystems of trainsets 30 3.2.2 Development of Subsystem-specific Degradation Model 30 3.3. Conceptualization of an Optimization Model 36 3.3.1 Objective Function 44 3.3.2 Constraints of MIP Model 45 3.4. Conceptualization of a Rule-based Heuristic Algorithm 50 CHAPTER 4 CASE STUDY 58 4.1. Description of Hsinchu Depot Empirical Case at TRA 59 4.2. Case I: Simplified Case for Rule-based Heuristic Algorithm Validation 68 4.3. Case II: Comparison of Plans Considering Different Maintenance Strategies 69 4.3.1 Case with particular train type (EMU500-only) 70 4.3.2 Case with particular train type (EMU700-only) 71 4.3.3 Consideration of both train types (500 + 700) 72 4.4. Case III: Sensitivity Analysis on Heuristic Algorithm 73 4.4.1 System Analysis under Different Failure Cost 73 4.4.2 System Analysis under Different Maintenance Rules 74 4.5. Summary and Discussion 76 CHAPTER 5 CONCLUSION AND FUTURE WORK 77 5.1. Conclusions 78 5.2. Future Work 79 REFERENCE 83 | - |
| dc.language.iso | en | - |
| dc.subject | 鐵路運輸 | zh_TW |
| dc.subject | 規則式啟發式演算法 | zh_TW |
| dc.subject | 最佳化方法 | zh_TW |
| dc.subject | 預測性檢修 | zh_TW |
| dc.subject | 檢修排程規劃 | zh_TW |
| dc.subject | 鐵路車輛調配 | zh_TW |
| dc.subject | 鐵路車輛子系統 | zh_TW |
| dc.subject | Trainset Subsystem | en |
| dc.subject | Rule-based Heuristic Algorithm | en |
| dc.subject | Maintenance Scheduling | en |
| dc.subject | Optimization Method | en |
| dc.subject | Predictive Maintenance | en |
| dc.subject | Rail Transportation | en |
| dc.title | 考量鐵路車輛子系統劣化特性之車輛指派與檢修排程模式 | zh_TW |
| dc.title | Train-set Assignment and Maintenance Scheduling Model considering Deterioration Characteristics of Rolling Stock Subsystems | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 黃奎隆;鍾志成;黃笙玹 | zh_TW |
| dc.contributor.oralexamcommittee | Kwei-Long Hunag;Jyh-Cherng Jong;Sheng-Hsuan Huang | en |
| dc.subject.keyword | 鐵路運輸,鐵路車輛子系統,鐵路車輛調配,檢修排程規劃,預測性檢修,最佳化方法,規則式啟發式演算法, | zh_TW |
| dc.subject.keyword | Rail Transportation,Trainset Subsystem,Maintenance Scheduling,Predictive Maintenance,Optimization Method,Rule-based Heuristic Algorithm, | en |
| dc.relation.page | 89 | - |
| dc.identifier.doi | 10.6342/NTU202303687 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2023-08-10 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 土木工程學系 | - |
| 顯示於系所單位： | 土木工程學系 | |
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