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Forecasting study of mains reliability based on sparse field data and perspective state space models

Treść / Zawartość
Identyfikatory
Warianty tytułu
PL
Prognozowanie niezawodności elementów sieci wodociągowej na podstawie rzadkich danych terenowych i modeli przestrzeni stanów
Języki publikacji
EN
Abstrakty
EN
The elements of critical infrastructure have to meet demanding dependability, safety and security requirements. The article deals with the prognosis of water mains reliability while using sparse irregular filed data. The data are sparse because the only thing we know is the number of mains failures during a given month. Since it is possible to transform the data into a typical reliability measure (rate of failure occurrence – ROCOF), we can examine the course of this measure development in time. In order to model and predict the ROCOF development, we suggest novel single and multiple error state space models. The results can be used for i) optimizing mains operation and maintenance, ii) estimating life cycle cost, and iii) planning crisis management.
PL
Elementy infrastruktury krytycznej muszą spełniać wysokie wymagania w zakresie niezawodności, bezpieczeństwa i ochrony. Artykuł dotyczy prognozowania niezawodności sieci wodociągowej przy wykorzystaniu nieregularnie rejestrowanych rzadkich danych. Wykorzystane w pracy dane są rzadkie, ponieważ dostarczają jedynie informacji na temat liczby uszkodzeń wodociągu w danym miesiącu. Przekształcenie tych danych w typową miarę niezawodności (wskaźnik występowania uszkodzeń – ROCOF), pozwala zbadać przebieg rozwoju tej miary w czasie. Rozwój ROCOF można modelować i przewidywać za pomocą zaproponowanych w pracy innowacyjnych modeli przestrzeni stanów uwzględniających pojedynczy błąd lub wiele błędów. Uzyskane wyniki można wykorzystać do i) optymalizacji pracy i eksploatacji sieci wodociągowej, ii) szacowania kosztów cyklu życia, oraz iii) planowania zarządzania kryzysowego.
Rocznik
Strony
179--191
Opis fizyczny
Bibliogr. 40 poz., rys., tab.
Twórcy
autor
  • Department of Combat and Special Vehicles University of Defence Kounicova str. 65, 662-10 Brno, Czech Republic Faculty of Transport and Computer Science, University of Economics and Innovation, Projektowa 4, 20-209 Lublin, Poland, david.valis@unob.cz
  • Department of Statistics and Operation Analysis Mendel University in Brno Zemědělská 1, 61300 Brno, Czech Republic, mforbelska@gmail.com
  • Department of Combat and Special Vehicles University of Defence Kounicova str. 65, 662-10 Brno, Czech Republic, zdenek.vintr@unob.cz
Bibliografia
  • 1. Anderson B D O, Moore J B. Optimal filtering. Prentice Hall, New York: Englewood Cliffs 1979.
  • 2. Antholzer S, Haltmeier M, Schwab J. Deep learning for photoacoustic tomography from sparse data. Inverse Problems in Science and Engineering 2019; 27(7): 987-1005, https://doi.org/10.1080/17415977.2018.1518444.
  • 3. Box George E P, Cox D R. An analysis of transformations. Journal of the Royal Statistical Society 1964; 26(2): 211-252, https://doi.org/10.1111/j.2517-6161.1964.tb00553.x.
  • 4. Box George E P, Jenkins G M. Time Series Analysis: Forecasting and Control. San Francisco: Holden Day 1976.
  • 5. Brockwell P J, Davis R A. Time Series: Theory and Methods, Berlin: Springer 1991. https://doi.org/10.1007/978-1-4419-0320-4
  • 6. De Livera A M, Hyndman R J, Snyder R D. Forecasting time series with complex seasonal patterns using exponential smoothing. Journal of the American Statistical Association 2011; 106(496): 1513-1527, https://doi.org/10.1198/jasa.2011.tm09771.
  • 7. Dindarloo S. Reliability forecasting of a load-haul-dump machine: a comparative study of ARIMA and neural networks. Quality and Reliability Engineering International 2016; 32(4): 1545-1552, https://doi.org/10.1002/qre.1844.
  • 8. Distefano S, Longo F, Trivedi K S. Investigating dynamic reliability and availability through state-space models. Computers & Mathematics with Applications 2012; 64(12): 3701-3716, https://doi.org/10.1016/j.camwa.2012.02.038.
  • 9. Duan RX, Lin Y N, Zeng Y N, Fault diagnosis for complex systems based on reliability analysis and sensors data considering epistemic uncertainty. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2018; 20(4): 558-566, https://doi.org/10.17531/ein.2018.4.7.
  • 10. dos Santos TR, Gamerman D, Franco G D. Reliability Analysis via Non-Gaussian State-Space Models. IEEE Transactions on Reliability 2017; 66(2): 309-318, https://doi.org/10.1109/TR.2017.2670142.
  • 11. Faes L, Porta A, Javorka M, Nollo G. Efficient computation of multiscale entropy over short biomedical time series based on linear statespace models. Complexity 2017: 1768264, https://doi.org/10.1155/2017/1768264.
  • 12. Gardner J E S, McKenzie E. 1985. Forecasting trends in time series. Management Science 1985; 31(10): 1237-1246, https://doi.org/10.1287/mnsc.31.10.1237.
  • 13. Glowacz A. Fault diagnosis of single-phase induction motor based on acoustic signals. Mechanical Systems and Signal Processing 2019; 117: 65-80, https://doi.org/10.1016/j.ymssp.2018.07.044.
  • 14. Glowacz A. Fault Detection of Electric Impact Drills and Coffee Grinders Using Acoustic Signals. Sensors 2019; 19(2): 269, https://doi.org/10.3390/s19020269.
  • 15. Gobbato M, Kosmatka J B, Conte J P. A recursive Bayesian approach for fatigue damage prognosis: An experimental validation at the reliability component level. Mechanical Systems and Signal Processing 2014; 45(2): 448-467, https://doi.org/10.1016/j.ymssp.2013.10.014.
  • 16. Gobbato M, Conte J P, Kosmatka J B, Farrar C R. A reliability-based framework for fatigue damage prognosis of composite aircraft structures. Probabilistic Engineering Mechanics 2012; 29: 176-188, https://doi.org/10.1016/j.probengmech.2011.11.004.
  • 17. Guo, Y J, Zhao Z B, Sun R B, Chen X F. Data-driven multiscale sparse representation for bearing fault diagnosis in wind turbine. Wind Energy 2019; 22(4): 587-604, https://doi.org/10.1002/we.2309.
  • 18. Harvey A C. Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge: University Press 1989, https://doi.org/10.1017/CBO9781107049994.
  • 19. Hu Y W, Liu S J, Lu H T, Zhang H C. Remaining useful life model and assessment of mechanical products: a brief review and a note on the state space model method. Chinese Journal of Mechanical Engineering 2019; 32(1):15, https://doi.org/10.1186/s10033-019-0317-y.
  • 20. Hyndman R J, Koehler A B, Ord J K, Snyder R D. Forecasting with Exponential Smoothing: The State Space Approach. Berlin Germany: Springer 2008, https://doi.org/10.1007/978-3-540-71918-2.
  • 21. Hyndman R J, Koehler A B. Another look at measures of forecast accuracy. International Journal of Forecasting 2006; 22: 679-688, https:// doi.org/10.1016/j.ijforecast.2006.03.001.
  • 22. Hyndman R J, Koehler A B, Snyder R D, Grose S. A state space framework for automatic forecasting using exponential smoothing methods. International Journal of Forecasting 2002; 18: 439-454, https://doi.org/10.1016/S0169-2070(01)00110-8.
  • 23. Hyndman R, Athanasopoulos G, Bergmeir C, Caceres G, Chhay L, O'Hara-Wild M, Petropoulos F, Razbash S, Wang E, Yasmeen F (2019). Forecast: Forecasting functions for time series and linear models. R package version 8.9, <URL: http://pkg.robjhyndman.com/forecast>.
  • 24. Hyndman R J, Khandakar Y (2008). Automatic time series forecasting: the forecast package for R. Journal of Statistical Software 2008; 26(3): 1-22, https://doi.org/10.18637/jss.v027.i03.
  • 25. Jimenez Cortadi A, Irigoien I, Boto F, Sierra B, Suarez A, Galar D. A statistical data-based approach to instability detection and wear prediction in radial turning processes. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2018; 20(3): 405-412, https://doi.org/10.17531/ein.2018.3.8.
  • 26. Kontrec N Z, Milovanovic G V, Panic S R, Milosevic H. A reliability-based approach to nonrepairable spare part forecasting in aircraft maintenance system. Mathematical Problems in Engineering 2015: 731437, https://doi.org/10.1155/2015/731437.
  • 27. Koucký M, Vališ D. Reliability of sequential systems with a restricted number of renewals. London: Taylor & Francis ltd. Risk, Reliability and Societal Safety, Vols. 1-3, ESREL 2007 Stavanger; 1845-1851.
  • 28. Kozłowski E, Mazurkiewicz D, Żabinski T, Prucnal S, Sęp J. Assessment model of cutting tool condition for real-time supervision system. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2019; 21(4): 679-685, https://doi.org/10.17531/ein.2019.4.18.
  • 29. Li H K, Zhou S, Kan H L, Cong M. Milling cutter condition reliability prediction based on state space model. Journal of Vibroengineering 2016; 18(7): 4312-4328, https://doi.org/10.21595/jve.2016.16648.
  • 30. Pietrucha-Urbanik K, Studzinski A. Selected issues of costs and failure of pipes in an exemplary water supply system. Rocznik Ochrona Srodowiska 2016; 18: 616-627.
  • 31. R Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria (2015). URL https://www.R-project.org/ (accessed 16.06.03).
  • 32. Rymarczyk T, Klosowski G. Innovative methods of neural reconstruction for tomographic images in maintenance of tank industrial reactors. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2019; 21(2): 261-267, https://doi.org/10.17531/ein.2019.2.10.
  • 33. Snyder R D. Recursive estimation of dynamic linear models. Journal of the Royal Statistical Society 1985; 47(2): 272-276, https://doi.org/10.1111/j.2517-6161.1985.tb01355.x.
  • 34. Snyder R. Discussion. International Journal of Forecasting 2006; 22(4): 673-676, https://doi.org/10.1016/j.ijforecast.2006.05.002.
  • 35. Sousa H, Wang Y. Sparse representation approach to data compression for strain-based traffic load monitoring: A comparative study. Measurement 2018; 122: 630-637, https://doi.org/10.1016/j.measurement.2017.10.042.
  • 36. Vališ D, Mazurkiewicz D. Application of selected Levy processes for degradation modelling of long range mine belt using real-time data. Archives of Civil and Mechanical Engineering 2018; 18(4): 1430-1440, https://doi.org/10.1016/j.acme.2018.05.006.
  • 37. Vališ D, Žák L. Contribution to prediction of soft and hard failure occurrence in combustion engine using oil tribo data. Engineering Failure Analysis 2017; 82:583-598, https://doi.org/10.1016/j.engfailanal.2017.04.018.
  • 38. Vališ D, Mazurkiewicz D, Forbelská M. Modelling of a Transport Belt Degradation Using State Space Model. In: Proceedings of the 2017 IEEE International Conference on Industrial Engineering & Engineering Management. Singapur: IEEE, 2017: 949-953, https://doi.org/10.1109/IEEM.2017.8290032.
  • 39. Wang L, Lu Z R. Sensitivity-free damage identification based on incomplete modal data, sparse regularization and alternating minimization approach. Mechanical Systems and Signal Processing 2019; 120:43-68, https://doi.org/10.1016/j.ymssp.2018.10.013.
  • 40. Whittle P. Hypothesis Testing in Time Series Analysis. Uppsala: Almquist and Wicksell 1951.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ad6d3362-92ee-4c2a-9cbb-4b53904b3cac
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