Skip to main content

2022 | OriginalPaper | Buchkapitel

9. Unified Corporate Platform URRAN (UCP URRAN)

verfasst von : Igor Borisovich Shubinsky, Alexei Mikhailovitch Zamyshlaev

Erschienen in: Technical Asset Management for Railway Transport

Verlag: Springer International Publishing

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The management of technical assets of the Russian Railways is carried out on the basis of the URRAN system which is composed of three interrelated components:

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Avizienis, A., Laprie, J-C., Randell, B.: Dependability of computer systems. Fundamental concepts, terminology and examples. Technical report. LAAS – CNRS (2000) Avizienis, A., Laprie, J-C., Randell, B.: Dependability of computer systems. Fundamental concepts, terminology and examples. Technical report. LAAS – CNRS (2000)
2.
Zurück zum Zitat Podinovsky, V.V., Nogin, V.D.: Pareto-optimal’nye resheniya mnogokriterial’nyh zadach (Pareto-optimal solutions of multicriterion problems). Nauka, Glavnaya redakciya fiziko-matematicheskoj literatury, Moscow (1982) Podinovsky, V.V., Nogin, V.D.: Pareto-optimal’nye resheniya mnogokriterial’nyh zadach (Pareto-optimal solutions of multicriterion problems). Nauka, Glavnaya redakciya fiziko-matematicheskoj literatury, Moscow (1982)
3.
Zurück zum Zitat Lasisi, A., Attoh-Okine, N.: Principal components analysis and track quality index: a machine learning approach. Transp. Res. Part C Emerg. Technol. 91, 230–248 (2018)CrossRef Lasisi, A., Attoh-Okine, N.: Principal components analysis and track quality index: a machine learning approach. Transp. Res. Part C Emerg. Technol. 91, 230–248 (2018)CrossRef
4.
Zurück zum Zitat Thaduri, A., Galar, D., Kumar, U.: Railway assets: a potential domain for big data analytics. Proc. Comput. Sci. 53, 457–467 (2015)CrossRef Thaduri, A., Galar, D., Kumar, U.: Railway assets: a potential domain for big data analytics. Proc. Comput. Sci. 53, 457–467 (2015)CrossRef
5.
Zurück zum Zitat Famurewa, S.M., Zhang, L., Asplund, M.: Maintenance analytics for railway infrastructure decision support. Journal Qual. Maint. Eng. 23, 310–325 (2017)CrossRef Famurewa, S.M., Zhang, L., Asplund, M.: Maintenance analytics for railway infrastructure decision support. Journal Qual. Maint. Eng. 23, 310–325 (2017)CrossRef
6.
Zurück zum Zitat Nakhaee, M.C., Hiemstra, D., Stoelinga, M., van Noort, M.: The Recent Applications of Machine Learning in Rail Track Maintenance: A Survey. Lecture Notes in Computer Science. 91–105 (2019) Nakhaee, M.C., Hiemstra, D., Stoelinga, M., van Noort, M.: The Recent Applications of Machine Learning in Rail Track Maintenance: A Survey. Lecture Notes in Computer Science. 91–105 (2019)
7.
Zurück zum Zitat Goodfellow, I., Bengio, Y., Courville, A.: Glubokoe obuchenie (Deep Learning). DMK Press, Moscow (2018) Goodfellow, I., Bengio, Y., Courville, A.: Glubokoe obuchenie (Deep Learning). DMK Press, Moscow (2018)
8.
Zurück zum Zitat Cerrada, M., Zurita, G., Cabrera, D., Sánchez, R.V., Artés, M., Li, C.: Fault diagnosis in spur gears based on genetic algorithm and random forest. Mech. Syst. Signal Process. 70–71, 87–103 (2016)CrossRef Cerrada, M., Zurita, G., Cabrera, D., Sánchez, R.V., Artés, M., Li, C.: Fault diagnosis in spur gears based on genetic algorithm and random forest. Mech. Syst. Signal Process. 70–71, 87–103 (2016)CrossRef
9.
Zurück zum Zitat Santur, Y., Karakose, M., Akin, E.: Random forest based diagnosis approach for rail fault inspection in railways. National Conference on Electrical, Electronics and Biomedical Engineering. 714–719 (2016) Santur, Y., Karakose, M., Akin, E.: Random forest based diagnosis approach for rail fault inspection in railways. National Conference on Electrical, Electronics and Biomedical Engineering. 714–719 (2016)
10.
Zurück zum Zitat Chistyakov, S.P.: Sluchajnye lesa: obzor (Random forests: review.). Trudy Karel’skogo nauchnogo centra RAN. 1, 117–136 (2013) Chistyakov, S.P.: Sluchajnye lesa: obzor (Random forests: review.). Trudy Karel’skogo nauchnogo centra RAN. 1, 117–136 (2013)
11.
Zurück zum Zitat Hosmer, D., Lemeshov, S., Sturdivant, R.X.: Applied   logistic   regression. Wiley, New York (2013)CrossRef Hosmer, D., Lemeshov, S., Sturdivant, R.X.: Applied   logistic   regression. Wiley, New York (2013)CrossRef
12.
Zurück zum Zitat Hu, C., Liu, X.: Modeling Track Geometry Degradation Using Support Vector Machine Technique. 2016 Joint Rail Conference (2016) Hu, C., Liu, X.: Modeling Track Geometry Degradation Using Support Vector Machine Technique. 2016 Joint Rail Conference (2016)
13.
Zurück zum Zitat Shubinsky, I.B., Zamyshliaev, A.M., Pronevich, O.B., Platonov, E.N., Ignatov, A.N.: Application of machine learning methods for predicting hazardous failures of railway track assets. Dependability Journal. 2(73), 43–53 (2020)CrossRef Shubinsky, I.B., Zamyshliaev, A.M., Pronevich, O.B., Platonov, E.N., Ignatov, A.N.: Application of machine learning methods for predicting hazardous failures of railway track assets. Dependability Journal. 2(73), 43–53 (2020)CrossRef
14.
Zurück zum Zitat Zamyshliaev, A.M.: Premises of the creation of a digital traffic safety management system. Dependability Journal. 4(71), 45–52 (2019)CrossRef Zamyshliaev, A.M.: Premises of the creation of a digital traffic safety management system. Dependability Journal. 4(71), 45–52 (2019)CrossRef
15.
Zurück zum Zitat Druzhinin, G.V., Sergeeva, I.V.: Kachestvo informacii (The quality of information). Radio i svyaz’, Moscow (1990) Druzhinin, G.V., Sergeeva, I.V.: Kachestvo informacii (The quality of information). Radio i svyaz’, Moscow (1990)
16.
Zurück zum Zitat Shubinsky, I.B., Zamyshlyaev, A.M., Pronevich, O.B.: Graph method for evaluation of process safety in railway facilities. Dependability Journal. 17(1), 40–45 (2017)CrossRef Shubinsky, I.B., Zamyshlyaev, A.M., Pronevich, O.B.: Graph method for evaluation of process safety in railway facilities. Dependability Journal. 17(1), 40–45 (2017)CrossRef
17.
Zurück zum Zitat Zamyshlyaev, A.M.: Prikladnye informacionnye sistemy upravleniya nadezhnost’yu, bezopasnost’yu, riskami i resursami na zheleznodorozhnom transporte (Applied information systems for management of dependability, safety, risks and resources in railway transport). LLC “Journal Dependability”, Moscow (2013) Zamyshlyaev, A.M.: Prikladnye informacionnye sistemy upravleniya nadezhnost’yu, bezopasnost’yu, riskami i resursami na zheleznodorozhnom transporte (Applied information systems for management of dependability, safety, risks and resources in railway transport). LLC “Journal Dependability”, Moscow (2013)
Metadaten
Titel
Unified Corporate Platform URRAN (UCP URRAN)
verfasst von
Igor Borisovich Shubinsky
Alexei Mikhailovitch Zamyshlaev
Copyright-Jahr
2022
DOI
https://doi.org/10.1007/978-3-030-90029-8_9

Premium Partner