参考文献

[1] 侯秀芳, 冯晨, 左超, 等.2022年中国内地城市轨道交通线路概况[J].都市快轨交通, 2023, 36(1):9-13.

[2] 刁心宏, 李明华.城市轨道交通概论[M].北京：中国铁道出版社, 2009.

[3] 郑刚.华盛顿地铁追尾事故的分析与启示[J].现代城市轨道交通, 2009(5):77-79.

[4] 陈卓.城市轨道交通设备维修策略研究[D].南京：东南大学, 2020.

[5] GFMAM.The Maintenance Framework [M].American: GF- MAM, 2016.

[6] Ran Yongyi, Zhou Xin, Lin Pengfeng, et al.A Survey of Predictive Maintenance: Systems, Purposes and Approaches[J].IEEE Communications Surveys & Tutorials, 2019.

[7] 李葆文.设备管理新思维新模式[M].北京：机械工业出版社, 2010.

[8] 王小峰.基于RCM的铁路牵引供电设备维修模式的研究[D].成都：西南交通大学, 2008.

[9] 于娜.基于RCM的空调设备维修管理系统研究[D].南昌：南昌大学, 2014.

[10] 潘莹.基于RCM的地铁车辆维保技术研究[J].上海节能, 2022(10): 1309-1314.

[11] 潘丽莎, 龚玲, 冒玲丽.城市轨道交通车辆关键系统可靠性研究[J].城市轨道交通, 2012(7):80-83.

[12] 龚玲, 胡文伟.地铁车辆信息网络数据库在车辆维修管理中的应用[J].电力机车技术, 2002(2):28-29.

[13] 程祖国, 王居宽, 陈鞍龙, 等.城市轨道交通车辆部件故障与均衡修修程周期[J].城市轨道交通研究, 2006(1):46-49.

[14] 陈城辉, 徐永能, 王海峻, 等.基于可靠性的南京地铁车辆维修模式的应用[J].城市轨道交通研究, 2010, 13(11):84-87.

[15] 程祖国, 朱士友, 苏钊颐, 等.地铁列车系统修维修策略[J].城市轨道交通研究, 2018, 21(9):8-11.

[16] Chang M G, Lee J S.Early Stage Data-Based Probabilistic Wear Life Prediction and Maintenance Interval Optimization of Driving Wheels[J].Reliability Engineering & System Safety, 2020, 197:106791.

[17] Palese Joseph W, Zarembski Allan M, Ebersole Kyle.Stochastic Analysis of Transit Wheel Wear and Optimized Forecasting of Wheel Maintenance Requirements[C].Proceedings of the 2019 Joint Rail Conference, 2019:1-9.

[18] Wang Ling, Xu Hong, Yuan Hua, et al.Optimizing the Re-Profiling Strategy of Metro Wheels Based on a Data-Driven Wear Model[J].European Journal of Operational Research, 2015, 242(3):975-986.

[19] Zeng Yuanchen, Song Dali, Zhang Weihua, et al.A New Physics-Based Data-Driven Guideline for Wear Modelling and Prediction of Train Wheels Science Direct[J].Wear, 2020:456-457.

[20] Zhu Wei, Yang Di, Guo Zhongkai, et al.Data-Driven Wheel Wear Modeling and Reprofiling Strategy Optimization for Metro Systems[C].Transportation Research Record: Journal of the Transportation Research Board, No.2476, Transportation Research Board, Washington, D.C., 2015: 67-76.

[21] 王凌, 员华, 那文波, 等.基于磨耗数据驱动模型的轮对镟修策略优化和剩余寿命预报[J].系统工程理论与实践, 2011, 31(6):10.

[22] Ignesti M, Innocenti A, Marini L, et al.Development of a Wear Model for the Wheel Profile Optimisation on Railway Vehicles[J].Vehicle System Dynamics, 2013, 51(9):1363-1402.

[23] Kou Jie, Zhang Jinmin, Zhou Hechao, et al.Effect of the Worn Status of Wheel/Rail Profiles on Wheel Wear over Curved Tracks[J].Journal of Mechanical Science and Technology, 2021, 35(3):945-954.

[24] Liu Bin, Lin Jing, Zhang Liangwei, et al.A Dynamic Prescriptive Maintenance Model Considering System Aging and Degradation[J].IEEE Access, 2019,7:94931-94943.

[25] Umamaheswari R, Chitra S, Kavitha D.Reliability Analysis and Dynamic Maintenance Model Based on Fuzzy Degradation Approach[J].Soft Computing, 2021, 25(5):3577-3592.

[26] Lin Jing, Pulido Julio, Asplund Matthisa.Reliability Analysis for Preventive Maintenance Based on Classical and Bayesian Semi-Parametric Degradation Approaches using Locomotive Wheel-Sets as a Case Study[J].Reliability Engineering & System Safety, 2015, 134:143-156.

[27] Shen Ruiyuan, Wang Tiantian, Luo Qizhang.Towards Prognostic and Health Management of Train Wheels in the Chinese Railway Industry[J].IEEE Access, 2019, 7:115292-115303.

[28] Zhu W, Xiao X, Huang Z D, et al.Evaluating the Wheelset Health Status of Rail Transit Vehicles: Synthesis of Wear Mechanism and Data-Driven Analysis[J].Journal of Transportation Engineering Part A Systems, 2020, 146(12):04020139.

[29] Kijima Masaaki, Nakagawa Toshio.Replacement Policies of a Shock Model with Imperfect Preventive[J].European Journal of Operational Research, 1992, 57(1):100-110.

[30] E Mingcheng, Li Bing, Jiang Zengqiang, et al.An Optimal Reprofiling Policy for High-Speed Train Wheels Subject to Wear and External Shocks Using a Semi-Markov Decision Process[J].IEEE Transactions on Reliability, 2018, 67(4):1468-1481.

[31] Liang Ce, et al.A Maintenance Model for High Speed Train Wheel Subject to Internal Degradation and External Shocks[C].Prognostics and System Health Management Conference, PHM-Harbin, 2017.

[32] Fernando Pascual, J A Marcos.Wheel Wear Management on High-speed Passenger Rail: A Common Playground for Design and Maintenance Engineering in the Talgo Engineering Cycle[C].Rail Conference.IEEE, 2004.

[33] Zio E, Compare M.Evaluating Maintenance Policies by Quantitative Modeling and Analysis[J].Reliability Engineering & System Safety, 2013,109:53-65.

[34] Lin S, Li N, Feng D, et al.A Preventive Opportunistic Maintenance Method for Railway Traction Power Supplysystem Based on Equipment Reliability[J].Railway Engineering Science, 2020, 33(20):129-136.

[35] 皇甫小燕.城市轨道交通车辆全寿命周期成本探讨[J].城市轨道交通研究, 2012, 5:8-11.

[36] 孙楠楠.以可靠性为中心的高铁接触网预防性机会维护研究[D].南昌：华东交通大学, 2018.

[37] 何勇.分阶段成组维护策略在动车组部件预防性维护中的研究[D].兰州：兰州交通大学, 2017.

[38] 戈春珍.基于故障数据分析的地铁车辆检修策略优化[D].北京：北京交通大学, 2018.

[39] 杨国军, 王红, 何勇, 等.故障及经济相关下动车组系统动态成组维护策略[J].铁道科学与工程学报, 2021, 18(1):31-37.

[40] 邵新杰, 曹立军, 田广, 等.复杂装备故障预测与健康管理技术[M].北京：国防工业出版社, 2013:23-45.

[41] 王道平, 张义忠.故障智能诊断系统的理论与方法[M].北京：冶金工业出版社, 2001.

[42] 马伦, 康建设, 赵春宇, 等.武器装备故障预测建模方法选择研究[J].计算机应用研究, 2013, 30(7):1929-1938.

[43] Pecht M.Prognostics and Health Management of Electronics[M].Hoboken, NJ, USA: Hoboken: John Wiley and Sons, 2008.

[44] Gu J, Barker D, Pecht M.Prognostics Implementation of Electronics under Vibration Loading[J].Microelectronics Reliability, 2007, 47(12):1849-1856.

[45] 张小丽.机械重大装备寿命预测综述[J].机械工程学报, 2011, 41(17):100-116.

[46] Kacprzynski G J, Sarlashkar A, Roemer M J, et al.Predicting Remaining Life by Fusing the Physics of Failure Modeling with Diagnostics[J].JOm, 2004, 56(3):29-35.

[47] Si X S, Wang W, Hu C H, et al.Remaining Useful Life Estimation - A Review on the Statistical Data Driven Approaches[J].European Journal of Operational Research, 2011, 213(1):1-14.

[48] 胡昌华, 施权, 司小胜, 等.数据驱动的寿命预测和健康管理技术研究进展[J].信息与控制, 2017(1):72-82.

[49] Lu C J, Meeker W O.Using Degradation Measures to Estimate a Time-to-Failure Distribution[J].Technometrics, 1993, 35(2):161-174.

[50] Gebraeel N.Sensory-Updated Residual life Distributions for Components with Exponential Degradation Pattern[J].IEEE Transactions on Automation Science and Engineering, 2006, 3(4):382-393.

[51] Liu Z, Li Q, Mu C.A Hybrid LSSV R-HMM Based Prognostics Approach[C].//4th International Conference on Intelligent Human-Machine Systems and Cybernetics.Piscataway, NJ, USA: IEEE, 2012:275-278.

[52] Soualhi A, Razik H, Clerc G, et al.Prognosis of Bearing Failures Using Hidden Markov Models and the Adaptive Neuro-fuzzy Inference System[J].IEEE Transactions on Industrial Electronics, 2014, 61(6):2864-2874.

[53] Wang X, Xu D.An Inverse Gaussian Process Model for Degradation Data[J].Technometrics, 2010, 52(2):188-197.

[54] 李烁.考虑测量误差的逆高斯过程退化建模与加速退化试验设计[D].上海：上海交通大学, 2018.

[55] Abdel-Hameed M.A Gamma Wear Process[J].IEEE Transactions on Reliability, 1975, 24(2):152-153.

[56] 尚洁.复杂应力下产品性能退化分析方法研究[D].杭州：浙江大学, 2016.

[57] Hu C H, Shi Q, Si X S, et al.Data-driven Life Prediction and Health Management: State of the Art[J].Information and Control, 2017, 46(1):72-82.

[58] Hu Y G, Li H, Liao X L, et al.Performance Degradation Model and Prediction Method of Real-Time Remaining Life for Wind Turbine Bearings[J].Proceedings of the CSEE, 2016, 36(6):1643-1649.

[59] Hu Y , Li H , Shi P , et al.A Prediction Method for the Real-time Remaining Useful Life of Wind Turbine Bearings Based on the Wiener Process[J].Renewable energy, 2018, 127:452-460.

[60] Di Maio F, Tsui K L, Zio E.Combining Relevance Bector Machines and Exponential Regression for Bearing Residual Life Estimation[J].Mechanical Systems and Signal Processing, 2012, 31(8):405-427.

[61] 冯鹏飞, 朱永生, 王培功, 等.基于相关向量机模型的设备运行可靠性预测[J].振动与冲击, 2017, 36(12):146-149.

[62] Liu J, Saxenaa, Kai G, et al.An Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries[C].Annual Conference of the Prognostics and Health Management Society, 2010:1-9.

[63] Yuanhang Chen, Gaoliang Peng, Zhiyu Zhu, Sijue Li.A Novel Deep Learning Method Based on Attention Mechanism for Bearing Remaining Useful Life Prediction[J].Applied Soft Computing Journal, 2019, 86:1-11.

[64] Saha B, Goebel K, Poll S, et al.Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework[J].Instrumentation and Measurement, IEEE Transactions on, 2009, 58(2):291-296.

[65] Huang R, Xi L, Li X, et al.Residual Life Predictions for Ball Bearings Based on Self-organizing Map and Back Propagation Neural Network Methods[J].Mechanical Systems and Signal Processing, 2007, 21(1):193-207.

[66] Taylor S J, Letham B.Forecasting at Scale[R].PeerJ Preprints, 2017.

[67] Hochreiter S, Schmidhuber J.Long Short-term Memory[J].Neural Computation, 1997, 9(8):1735-1780.

[68] Croston J D.Forecasting and Stock Control for Intermittent Demands[J].Operational Research Quarterly (1970-1977), 1972, 23(3):289-303.

[69] Syntetos A A, Boylan J E, Croston J D.On the Categorization of Demand Patterns[J].Journal of the Operational Research Society, 2005, 56(5):495-503.

[70] Syntetos A A, Boylan J E.The Accuracy of Intermittent Demand Estimates[J].International Journal of Forecasting, 2005, 21(2):303-314.

[71] Babai M Z, Dallery Y, Boubaker S, et al.A New Method to Forecast Intermittent Demand in the Presence of Inventory Obsolescence[J].International Journal of Production Economics, 2019, 209:30-41.

[72] Teunter R H, Syntetos A A, Zied Babai M.Intermittent Demand: Linking Forecasting to Inventory Obsolescence[J].European Journal of Operational Research, 2011, 214(3):606-615.

[73] Willemain T R, Smart C N, Schwarz H F.A New Approach to Forecasting Intermittent Demand for Service Parts Inventories[J].International Journal of Forecasting, 2004, 20(3):375-387.

[74] Porras E, Dekker R.An Inventory Control System for Spare Parts at a Refinery: An Empirical Comparison of Different Re-order Point Methods[J].European Journal of Operational Research, 2008, 184(1):101-132.

[75] Snyder R.Forecasting Sales of Slow and Fast Moving Inventories[J].European Journal of Operational Research, 2002, 140(3):684-699.

[76] Hua Z, Zhang B.A Hybrid Support Vector Machines and Logistic Regression Approach for Forecasting Intermittent Demand of Spare Parts[J].Applied Mathematics and Computation, 2006, 181(2):1035-1048.

[77] Wu P, Hung Y Y, Lin Z Po.Intelligent Forecasting System Based on Integration of Electromagnetism-Like Mechanism and Fuzzy Neural Network[J].Expert Systems with Applications, 2014, 41(6):2660-2677.

[78] Nikolopoulos K, Syntetos A A, Boylan J E, et al.An Aggregate-disaggregate Intermittent Demand Approach(ADIDA) to Forecasting: AnEmpirical Proposition and Analysis[J].Journal of the Operational Research Society, 2011, 62(3):544-554.

[79] Petropoulos F, Kourentzes N.Forecast Combinations for Intermittent Demand[J].Journal of the Operational Research Society, 2015, 66(6):914-924.

[80] Pinçe Ç, Turrini L, Meissner J.Intermittent Demand Forecasting for Spare Parts: A Critical Review[J].Omega, 2021, 105:102513.

[81] Hu Q, Boylan J E, Chen H, et al.OR in Spare Parts Management: A Review[J].European Journal of Operational Research, 2018, 266(2):395-414.

[82] Arvan M, Fahimnia B, Reisi M, et al.Integrating Human Judgement into Quantitative Forecasting Methods: A Review[J].Omega, 2019, 86:237-252.

[83] Croson R, Schultz K, Siemsen E, et al.Behavioral Operations: The State of the Field[J].Behavioral Operations, 2013, 31(1):1-5.

[84] Akaike H.A New Look at the Statistical Model Identification[J].IEEE Transactions on Automatic Control, 1974, 19(6):716-723.

[85] Petropoulos F, Siemsen E.Forecast Selection and Representativeness[J].Management Science, 2022.

[86] Petropoulos F, Apiletti D, Assimakopoulos V, et al.Forecasting: Theory and Practice[J].International Journal of Forecasting, 2022, 38(3):705-871. xNOsYUyhL0Pc5OQ/Uu8Mtvp4Rp5RapYjfvRpQDzWjY3+5bDfxwekV77FGiJK6mmH