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An Artificial Neural Network Model for Maintenance Planning of Metro Trains

Yıl 2021, Cilt: 24 Sayı: 3, 811 - 820, 01.09.2021
https://doi.org/10.2339/politeknik.693223

Öz

In urban transportation, trains have an increasingly important place due to the increase in the number of passengers. Meeting the number of passengers is directly related to the number of trains operated on a line. Thus, the frequency of operation of trains affects the level of wear of the equipment. This makes train maintenance more important. Equipment faults are the basis for train maintenance. However, the fault times of the equipment which are unknown causes uncertainty in the maintenance activities and plans. This uncertainty results from many factors that affect the faults of the train. If historical maintenance data, fault data, and factors affecting the faults are known, effective use of resources (time, cost and personnel, etc.) is provided and uncertainty is eliminated. In this study, firstly, maintenance data in Ankara Metro between 2017 and 2018 is examined and the factors affecting equipment faults are evaluated with expert opinion. Artificial Neural Network (ANN) model is created with the data set and this data set along with the factors affecting each the equipment fault according to the type of equipment. In the ANN model, 5 factors (Equipment Type, Preventive Maintenance Frequency, Material Quality, Life Cycle, Line Status) affecting the faults of the equipment is determined as inputs and the number of failures as outputs. The mean absolute percent error (MAPE) value is found as 11%, and the mean square error value (MSE) is 0.0028229 in the training and test stages of ANN. Then, the frequency of fault is found according to the equipment fault and a 10-week maintenance planning is applied. The results are compared with current maintenance planning. As a result of the applied maintenance planning, the average number of faults of the trains decreases by 27%, uninterrupted service rate increases by 40% and heavy maintenance errors are also prevented. Fault removal times resulted in a 10% improvement. The results showed that ANN models could be used effectively in fault prediction and maintenance planning with rail system multiple types of equipment. In the literature, there is no study that implements maintenance planning with an ANN model where all train equipments and factors affecting the failure are evaluated together. This study is the first in the field of rail systems maintenance in the literature and will be a reference for future studies. 

Kaynakça

  • [1] Eren T., Gencer M.A., "Ankara Metrosu M1 (Kızılay-Batıkent) Hattı Hareket Saatlerinin Çizelgelenmesi", Akademik Platform Mühendislik ve Fen Bilimleri Dergisi, 4: 2, (2016).
  • [2] Düzakın E., Demircioğlu M., "Bakım Stratejileri ve Bekleme Hattı Modeli Uygulaması", Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 14(1): 211-230, (2005).
  • [3] Rao BKN, "Handbook of condition monitoring", Elsevier, (1996).
  • [4] Rausand M., "Reliability centered maintenance", Reliab Eng Syst Saf; 60:121–32, (1998).
  • [5] Albert H.C., Tsang, "Condition-Based Maintenance: Tools and Decision Making", J Qual Maint Eng, 1: 3–17, (1995).
  • [6] Ahmad R., Kamaruddin S., "An overview of time-based and condition-based maintenance in industrial application", Comput Ind Eng; 63: 135–49, (2012).
  • [7] Campos J., "Computers in Industry Development in the application of ICT in condition monitoring and maintenance", Computers in industry, 60: 1–20, (2009).
  • [8] Ali J.M., Hussain M.A., Tade M.O., Zhang J., "Artificial Intelligence techniques applied as an estimator in chemical process systems–A literature survey", Expert Syst Appl, 42: 5915–31, (2015).
  • [9] Penicka M., Strupchanska A., Bjorner D., "Train Maintenance Routing", (2003).
  • [10] Nitti M., Ruvo De P., Marino F., Stella E., Distante A., Ruvo De G., “A Visual Inspection System for Rail Detection and Tracking in Real-Time Railway Maintenance”, Open Cybern Syst, 2: 57-67, (2008).
  • [11] Yin J., Zhao W., “Fault diagnosis network design for vehicle onboard equipments of high-speed railway: A deep learning approach”, Eng Appl Artif Intell, 56: 250–9, (2016).
  • [12] De Bruin T., Verbert K., Babuška R., “Railway track circuit fault diagnosis using recurrent neural networks”, IEEE Trans Neural Networks Learn Syst, 28: 523–33, (2017).
  • [13] Shebani A., Iwnicki S., “Prediction of wheel and rail wear under different contact conditions using artificial neural networks”, Wear, 406: 173-184, (2018).
  • [14] Kaewunruen S., “Monitoring of rail corrugation growth on sharp curves for track maintenance prioritization”, International Journal of Acoustics and Vibration, 23(1): 35-43, (2018).
  • [15] Zhao Y., Guo Z. H., Yan J.M., “Vibration signal analysis and fault diagnosis of bogies of the high-speed train based on deep neural networks”, Journal of vibroengineering, 19(4): 2456-2474, (2017).
  • [16] Gibert X., Patel V.M., Chellappa R., “Deep multitask learning for railway track inspection”, IEEE transactions on intelligent transportation systems, 18(1): 153-164, (2016).
  • [17] Tam H.Y., Lee K.K., Liu S.Y., Cho L.H., Cheng K.C., “Intelligent Optical Fibre Sensing Networks Facilitate Shift to Predictive Maintenance in Railway Systems”. In 2018 International Conference on Intelligent Rail Transportation (ICIRT) (pp. 1-4), IEEE, December, (2018).
  • [18] Lidén T., Joborn M., “An optimization model for integrated planning of railway traffic and network maintenance”, Transportation Research Part C: Emerging Technologies, 74: 327-347, (2017).
  • [19] Luan X., Miao J., Meng L., Corman F., Lodewijks G., “Integrated optimization on train scheduling and preventive maintenance time slots planning”, Transportation Research Part C: Emerging Technologies, 80: 329-359, (2017).
  • [20] Su Z., Jamshidi A., Núñez A., Baldi S., De Schutter B., “Multi-level condition-based maintenance planning for railway infrastructures–A scenario-based chance-constrained approach”, Transportation Research Part C: Emerging Technologies, 84: 92-123, (2017).
  • [21] Baldi M.M., Heinicke F., Simroth A., Tadei R., “New heuristics for the stochastic tactical railway maintenance problem”, Omega, 63: 94-102, (2016).
  • [22] Zhang C., Gao Y., Yang L., Kumar U., Gao Z., “Integrated optimization of train scheduling and maintenance planning on high-speed railway corridors”, Omega, 87: 86-104, (2019).
  • [23] Consilvio A., Di Febbraro A., Sacco N., “Stochastic scheduling approach for predictive risk-based railway maintenance”, In 2016 IEEE international conference on intelligent rail transportation (ICIRT), 197-203, IEEE, August, (2016).
  • [24] Peralta D., Bergmeir C., Krone M., Galende M., Menéndez M., Sainz-Palmero G.I., Benitez J.M., “Multiobjective optimization for railway maintenance plans”, Journal of Computing in Civil Engineering, 32(3): (2018).
  • [25] Xu Y.N., Qiao Q., Wu R.F., Zhou Z.P., “Advanced maintenance cycle optimization of urban rail transit vehicles”, Advances in Mechanical Engineering, 11(2): (2019).
  • [26] Verbert K., De Schutter B., Babuška R., “Timely condition-based maintenance planning for multi-component systems”, Reliability Engineering & System Safety, 159: 310-321, (2017).
  • [27] Kaewunruen S., Chiengson C., “Railway track inspection and maintenance priorities due to dynamic coupling effects of dipped rails and differential track settlements”, Engineering Failure Analysis, 93: 157-171, (2018).
  • [28] Sharma S., Cui Y., He Q., Mohammadi R., Li Z., “Data-driven optimization of railway maintenance for track geometry”, Transportation Research Part C: Emerging Technologies, 90: 34-58, (2018).
  • [29] Macedo R., Benmansour R., Artiba A., Mladenović N., Urošević D., “Scheduling preventive railway maintenance activities with resource constraints”, Electronic Notes in Discrete Mathematics, 58, 215-222, (2017).
  • [30] Li J., Lin B., Wang Z., Chen L., Wang J., “A pragmatic optimization method for a motor train set assignment and maintenance scheduling problem”, Discrete Dynamics in Nature and Society, (2016).
  • [31] Yun W.Y., Han Y.J., Park G., “Optimal preventive maintenance interval and spare parts number in a rolling stock system”, In 2012 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, 380-384, IEEE, June, (2012).
  • [32] Lin C.C., Tseng H.Y., “A neural network application for reliability modeling and condition-based predictive maintenance”, Int J Adv Manuf Technol, 25: 174–9, (2005). doi:10.1007/s00170-003-1835-3,
  • [33] Wu S.J., Gebraeel N., Lawley M.A., Yih Y.., “A neural network integrated decision support system for condition-based optimal predictive maintenance policy”, IEEE Trans Syst Man, Cybern Part A Systems Humans, 37:226–36, (2007).
  • [34] Şimşir F., Ekmekci D., Kaçamak H., “Yapay Sinir Ağı Yardımıyla Araçların Değişik Yol Koşullarındaki Ortalama Hız Tahmini”, In 2015 3rd International Symposium On Innovative Technologies In Engineering And Science (ISITES2015), 3-5 June, Valencia, Spain, (2015).
  • [35] Park S., Kim M., Kim M., Namgung H.G., Kim K.T., Cho K.H., Kwon S.B., “Predicting PM10 concentration in Seoul metropolitan subway stations using artificial neural network (ANN)”, Journal of hazardous materials, 341: 75-82, (2018).
  • [36] Özcan E.C., Danışan T, Yumuşak R, Eren T., "An artificial neural network model supported with multi criteria decision making approaches for maintenance planning in hydroelectric power plants.", Eksploatacja i Niezawodnosc – Maintenance and Reliability, 21(3): 400–418, (2020).
  • [37] Doğan E., Akgüngör A.P., “Forecasting highway casualties under the effect of railway development policy in Turkey using artificial neural networks”, Neural computing and applications, 22(5): 869-877, (2013).
  • [38] Gürgen S., Ünver B., Altın İ., “Prediction of cyclic variability in a diesel engine fueled with n-butanol and diesel fuel blends using artificial neural networks”, Renewable Energy, 117:538-544, (2018).
  • [39] Gjordeni K., Kaya A., “Digitizing the Maintenance Management Operation: Exploring the Opportunities of an Information System in a Railway Maintenance Organization”, Master of Science Thesis, School Of Industrıal Engıneerıng And Management, Kth Royal Instıtute Of Technology, (2019).
  • [40] Bury H., Spieckermann S., Wortmann D., Hübler, F., “A case study on simulation of railway fleet maintenance”, In 2018 Winter Simulation Conference (WSC), 2851-2860, IEEE, December, (2018).
  • [41] Witt S. and Witt C., “Modeling and Forecasting Demand in Tourism”, Academic Press, London, 137, (1992).

An Artificial Neural Network Model for Maintenance Planning of Metro Trains

Yıl 2021, Cilt: 24 Sayı: 3, 811 - 820, 01.09.2021
https://doi.org/10.2339/politeknik.693223

Öz

In urban transportation, trains have an increasingly important place due to the increase in the number of passengers. Meeting the number of passengers is directly related to the number of trains operated on a line. Thus, the frequency of operation of trains affects the level of wear of the equipment. This makes train maintenance more important. Equipment faults are the basis for train maintenance. However, the fault times of the equipment which are unknown causes uncertainty in the maintenance activities and plans. This uncertainty results from many factors that affect the faults of the train. If historical maintenance data, fault data, and factors affecting the faults are known, effective use of resources (time, cost and personnel, etc.) is provided and uncertainty is eliminated. In this study, firstly, maintenance data in Ankara Metro between 2017 and 2018 is examined and the factors affecting equipment faults are evaluated with expert opinion. Artificial Neural Network (ANN) model is created with the data set and this data set along with the factors affecting each the equipment fault according to the type of equipment. In the ANN model, 5 factors (Equipment Type, Preventive Maintenance Frequency, Material Quality, Life Cycle, Line Status) affecting the faults of the equipment is determined as inputs and the number of failures as outputs. The mean absolute percent error (MAPE) value is found as 11%, and the mean square error value (MSE) is 0.0028229 in the training and test stages of ANN. Then, the frequency of fault is found according to the equipment fault and a 10-week maintenance planning is applied. The results are compared with current maintenance planning. As a result of the applied maintenance planning, the average number of faults of the trains decreases by 27%, uninterrupted service rate increases by 40% and heavy maintenance errors are also prevented. Fault removal times resulted in a 10% improvement. The results showed that ANN models could be used effectively in fault prediction and maintenance planning with rail system multiple types of equipment. In the literature, there is no study that implements maintenance planning with an ANN model where all train equipments and factors affecting the failure are evaluated together. This study is the first in the field of rail systems maintenance in the literature and will be a reference for future studies. 

Kaynakça

  • [1] Eren T., Gencer M.A., "Ankara Metrosu M1 (Kızılay-Batıkent) Hattı Hareket Saatlerinin Çizelgelenmesi", Akademik Platform Mühendislik ve Fen Bilimleri Dergisi, 4: 2, (2016).
  • [2] Düzakın E., Demircioğlu M., "Bakım Stratejileri ve Bekleme Hattı Modeli Uygulaması", Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 14(1): 211-230, (2005).
  • [3] Rao BKN, "Handbook of condition monitoring", Elsevier, (1996).
  • [4] Rausand M., "Reliability centered maintenance", Reliab Eng Syst Saf; 60:121–32, (1998).
  • [5] Albert H.C., Tsang, "Condition-Based Maintenance: Tools and Decision Making", J Qual Maint Eng, 1: 3–17, (1995).
  • [6] Ahmad R., Kamaruddin S., "An overview of time-based and condition-based maintenance in industrial application", Comput Ind Eng; 63: 135–49, (2012).
  • [7] Campos J., "Computers in Industry Development in the application of ICT in condition monitoring and maintenance", Computers in industry, 60: 1–20, (2009).
  • [8] Ali J.M., Hussain M.A., Tade M.O., Zhang J., "Artificial Intelligence techniques applied as an estimator in chemical process systems–A literature survey", Expert Syst Appl, 42: 5915–31, (2015).
  • [9] Penicka M., Strupchanska A., Bjorner D., "Train Maintenance Routing", (2003).
  • [10] Nitti M., Ruvo De P., Marino F., Stella E., Distante A., Ruvo De G., “A Visual Inspection System for Rail Detection and Tracking in Real-Time Railway Maintenance”, Open Cybern Syst, 2: 57-67, (2008).
  • [11] Yin J., Zhao W., “Fault diagnosis network design for vehicle onboard equipments of high-speed railway: A deep learning approach”, Eng Appl Artif Intell, 56: 250–9, (2016).
  • [12] De Bruin T., Verbert K., Babuška R., “Railway track circuit fault diagnosis using recurrent neural networks”, IEEE Trans Neural Networks Learn Syst, 28: 523–33, (2017).
  • [13] Shebani A., Iwnicki S., “Prediction of wheel and rail wear under different contact conditions using artificial neural networks”, Wear, 406: 173-184, (2018).
  • [14] Kaewunruen S., “Monitoring of rail corrugation growth on sharp curves for track maintenance prioritization”, International Journal of Acoustics and Vibration, 23(1): 35-43, (2018).
  • [15] Zhao Y., Guo Z. H., Yan J.M., “Vibration signal analysis and fault diagnosis of bogies of the high-speed train based on deep neural networks”, Journal of vibroengineering, 19(4): 2456-2474, (2017).
  • [16] Gibert X., Patel V.M., Chellappa R., “Deep multitask learning for railway track inspection”, IEEE transactions on intelligent transportation systems, 18(1): 153-164, (2016).
  • [17] Tam H.Y., Lee K.K., Liu S.Y., Cho L.H., Cheng K.C., “Intelligent Optical Fibre Sensing Networks Facilitate Shift to Predictive Maintenance in Railway Systems”. In 2018 International Conference on Intelligent Rail Transportation (ICIRT) (pp. 1-4), IEEE, December, (2018).
  • [18] Lidén T., Joborn M., “An optimization model for integrated planning of railway traffic and network maintenance”, Transportation Research Part C: Emerging Technologies, 74: 327-347, (2017).
  • [19] Luan X., Miao J., Meng L., Corman F., Lodewijks G., “Integrated optimization on train scheduling and preventive maintenance time slots planning”, Transportation Research Part C: Emerging Technologies, 80: 329-359, (2017).
  • [20] Su Z., Jamshidi A., Núñez A., Baldi S., De Schutter B., “Multi-level condition-based maintenance planning for railway infrastructures–A scenario-based chance-constrained approach”, Transportation Research Part C: Emerging Technologies, 84: 92-123, (2017).
  • [21] Baldi M.M., Heinicke F., Simroth A., Tadei R., “New heuristics for the stochastic tactical railway maintenance problem”, Omega, 63: 94-102, (2016).
  • [22] Zhang C., Gao Y., Yang L., Kumar U., Gao Z., “Integrated optimization of train scheduling and maintenance planning on high-speed railway corridors”, Omega, 87: 86-104, (2019).
  • [23] Consilvio A., Di Febbraro A., Sacco N., “Stochastic scheduling approach for predictive risk-based railway maintenance”, In 2016 IEEE international conference on intelligent rail transportation (ICIRT), 197-203, IEEE, August, (2016).
  • [24] Peralta D., Bergmeir C., Krone M., Galende M., Menéndez M., Sainz-Palmero G.I., Benitez J.M., “Multiobjective optimization for railway maintenance plans”, Journal of Computing in Civil Engineering, 32(3): (2018).
  • [25] Xu Y.N., Qiao Q., Wu R.F., Zhou Z.P., “Advanced maintenance cycle optimization of urban rail transit vehicles”, Advances in Mechanical Engineering, 11(2): (2019).
  • [26] Verbert K., De Schutter B., Babuška R., “Timely condition-based maintenance planning for multi-component systems”, Reliability Engineering & System Safety, 159: 310-321, (2017).
  • [27] Kaewunruen S., Chiengson C., “Railway track inspection and maintenance priorities due to dynamic coupling effects of dipped rails and differential track settlements”, Engineering Failure Analysis, 93: 157-171, (2018).
  • [28] Sharma S., Cui Y., He Q., Mohammadi R., Li Z., “Data-driven optimization of railway maintenance for track geometry”, Transportation Research Part C: Emerging Technologies, 90: 34-58, (2018).
  • [29] Macedo R., Benmansour R., Artiba A., Mladenović N., Urošević D., “Scheduling preventive railway maintenance activities with resource constraints”, Electronic Notes in Discrete Mathematics, 58, 215-222, (2017).
  • [30] Li J., Lin B., Wang Z., Chen L., Wang J., “A pragmatic optimization method for a motor train set assignment and maintenance scheduling problem”, Discrete Dynamics in Nature and Society, (2016).
  • [31] Yun W.Y., Han Y.J., Park G., “Optimal preventive maintenance interval and spare parts number in a rolling stock system”, In 2012 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, 380-384, IEEE, June, (2012).
  • [32] Lin C.C., Tseng H.Y., “A neural network application for reliability modeling and condition-based predictive maintenance”, Int J Adv Manuf Technol, 25: 174–9, (2005). doi:10.1007/s00170-003-1835-3,
  • [33] Wu S.J., Gebraeel N., Lawley M.A., Yih Y.., “A neural network integrated decision support system for condition-based optimal predictive maintenance policy”, IEEE Trans Syst Man, Cybern Part A Systems Humans, 37:226–36, (2007).
  • [34] Şimşir F., Ekmekci D., Kaçamak H., “Yapay Sinir Ağı Yardımıyla Araçların Değişik Yol Koşullarındaki Ortalama Hız Tahmini”, In 2015 3rd International Symposium On Innovative Technologies In Engineering And Science (ISITES2015), 3-5 June, Valencia, Spain, (2015).
  • [35] Park S., Kim M., Kim M., Namgung H.G., Kim K.T., Cho K.H., Kwon S.B., “Predicting PM10 concentration in Seoul metropolitan subway stations using artificial neural network (ANN)”, Journal of hazardous materials, 341: 75-82, (2018).
  • [36] Özcan E.C., Danışan T, Yumuşak R, Eren T., "An artificial neural network model supported with multi criteria decision making approaches for maintenance planning in hydroelectric power plants.", Eksploatacja i Niezawodnosc – Maintenance and Reliability, 21(3): 400–418, (2020).
  • [37] Doğan E., Akgüngör A.P., “Forecasting highway casualties under the effect of railway development policy in Turkey using artificial neural networks”, Neural computing and applications, 22(5): 869-877, (2013).
  • [38] Gürgen S., Ünver B., Altın İ., “Prediction of cyclic variability in a diesel engine fueled with n-butanol and diesel fuel blends using artificial neural networks”, Renewable Energy, 117:538-544, (2018).
  • [39] Gjordeni K., Kaya A., “Digitizing the Maintenance Management Operation: Exploring the Opportunities of an Information System in a Railway Maintenance Organization”, Master of Science Thesis, School Of Industrıal Engıneerıng And Management, Kth Royal Instıtute Of Technology, (2019).
  • [40] Bury H., Spieckermann S., Wortmann D., Hübler, F., “A case study on simulation of railway fleet maintenance”, In 2018 Winter Simulation Conference (WSC), 2851-2860, IEEE, December, (2018).
  • [41] Witt S. and Witt C., “Modeling and Forecasting Demand in Tourism”, Academic Press, London, 137, (1992).
Toplam 41 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Araştırma Makalesi
Yazarlar

Muhammed Abdullah Gençer 0000-0001-9955-5468

Rabia Yumuşak 0000-0002-0257-939X

Evrencan Özcan 0000-0002-3662-6190

Tamer Eren 0000-0001-5282-3138

Yayımlanma Tarihi 1 Eylül 2021
Gönderilme Tarihi 24 Şubat 2020
Yayımlandığı Sayı Yıl 2021 Cilt: 24 Sayı: 3

Kaynak Göster

APA Gençer, M. A., Yumuşak, R., Özcan, E., Eren, T. (2021). An Artificial Neural Network Model for Maintenance Planning of Metro Trains. Politeknik Dergisi, 24(3), 811-820. https://doi.org/10.2339/politeknik.693223
AMA Gençer MA, Yumuşak R, Özcan E, Eren T. An Artificial Neural Network Model for Maintenance Planning of Metro Trains. Politeknik Dergisi. Eylül 2021;24(3):811-820. doi:10.2339/politeknik.693223
Chicago Gençer, Muhammed Abdullah, Rabia Yumuşak, Evrencan Özcan, ve Tamer Eren. “An Artificial Neural Network Model for Maintenance Planning of Metro Trains”. Politeknik Dergisi 24, sy. 3 (Eylül 2021): 811-20. https://doi.org/10.2339/politeknik.693223.
EndNote Gençer MA, Yumuşak R, Özcan E, Eren T (01 Eylül 2021) An Artificial Neural Network Model for Maintenance Planning of Metro Trains. Politeknik Dergisi 24 3 811–820.
IEEE M. A. Gençer, R. Yumuşak, E. Özcan, ve T. Eren, “An Artificial Neural Network Model for Maintenance Planning of Metro Trains”, Politeknik Dergisi, c. 24, sy. 3, ss. 811–820, 2021, doi: 10.2339/politeknik.693223.
ISNAD Gençer, Muhammed Abdullah vd. “An Artificial Neural Network Model for Maintenance Planning of Metro Trains”. Politeknik Dergisi 24/3 (Eylül 2021), 811-820. https://doi.org/10.2339/politeknik.693223.
JAMA Gençer MA, Yumuşak R, Özcan E, Eren T. An Artificial Neural Network Model for Maintenance Planning of Metro Trains. Politeknik Dergisi. 2021;24:811–820.
MLA Gençer, Muhammed Abdullah vd. “An Artificial Neural Network Model for Maintenance Planning of Metro Trains”. Politeknik Dergisi, c. 24, sy. 3, 2021, ss. 811-20, doi:10.2339/politeknik.693223.
Vancouver Gençer MA, Yumuşak R, Özcan E, Eren T. An Artificial Neural Network Model for Maintenance Planning of Metro Trains. Politeknik Dergisi. 2021;24(3):811-20.
 
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