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Development of cause-effect dependence model of undesirable events using Bayes network

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In the paper the method of cause and effect analysis of undesirable events using the Bayesian networks is presented. For the analysis, due to the complexity of the calculations, it is proposed to use Java Bayes program as a free and simple tool to support Bayesian analysis. Bayesian estimation allows to identify the probability of the event occurrence. For this reason its use was proposed to determine the safety probability. Using Bayes' theorem is also possible to modify initial judgement about the situation with the use of a priori probability so that a new situation described by a posteriori probability arises. In this sense, by Bayes' theorem the data can be sequentially processed, including considerations for newer information, and thereby create a more reliable basis for decision making for the system operator. In the paper, the methodology was presented, which can be extended in order to improve the detection and monitoring of undesirable events in infrastructure.
Słowa kluczowe
Rocznik
Strony
149--156
Opis fizyczny
Bibliogr. 27 poz., rys., tab.
Twórcy
  • Rzeszow University of Technology, Rzeszow, Poland
  • Rzeszow University of Technology, Rzeszow, Poland
autor
  • Rzeszow University of Technology, Rzeszow, Poland
Bibliografia
  • [1] Apostolakis, G. & Kaplan, S. (1981). Pitfalls in risk calculations. Reliability Engineering and System Safety, 2, 135–145.
  • [2] Aven, T. (1992). Reliability and Risk Analysis. Copyright by Elsevier.
  • [3] Bernardo, J.M. & Smith, A.F.M. (1993). Bayesian theory. Wiley: Chichester.
  • [4] Billinton, R. & Allan, R.N. (1992). Reliability Evaluation in Engineering Systems. Concepts and Techniques. Copyright by Plenum Press. London.
  • [5] Birolini, A. (1990). Qualität und Zuverlässigkeit technischer Systems. Theorie, Praxis, Management. Copyright by Springer, Berlin.
  • [6] Bishop, C.M. (2006). Pattern Recognition and Machine Learning. Springer: New York.
  • [7] Blischke, W. & Murthy, D.N.P. (2000). Reliability: Modeling, Prediction and Optimization. Copyright by J. Wiley and Sons, New York.
  • [8] Drzazga, M., Kołowrocki, K., Soszyńska-Budny, J. & Torbicki, M. (2016). Port oil piping transportation critical infrastructure assets and interconnections. Journal of Polish Safety and Reliability Association. Summer Safety and Reliability Seminars, Vol 7, No 1, pp. 37-42.
  • [9] Dziula, P. & Kołowrocki, K. (2016). Identification of climate related hazards, the Global Baltic Network of Critical Infrastructure Networks, is exposed to. Journal of Polish Safety and Reliability Association. Summer Safety and Reliability Seminars, Vol 7, No 1, pp. 43-52.
  • [10] Grabski, F. & Jaźwiński, J. (2001). Metody bayesowskie w niezawodności i diagnostyce. Wydawnictwa Komunikacji i Łączności, Warszawa.
  • [11] Haimes, Y.Y. (1998). Risk Modelling, Assessment and Management. Wiley, New York.
  • [12] Hartig, J.A. (1983). Bayes theory. Springer, New York.
  • [13] Hubbard, D.W. (2009). The failure of risk management, Wiley. New York.
  • [14] Kołowrocki, K. & Soszyńska-Budny, J. (2011). Reliability and Safety of Complex Technical Systems and Processes: Modeling - Identification - Prediction - Optimization. Springer, London.
  • [15] Kuo, W. & Zuo, M. J. (2003). Optimal reliability modeling. Copyright by Wiley, New Jersey.
  • [16] Pham, H. (2003) Handbook of Reliability Engineering. Springer, London.
  • [17] Pietrucha-Urbanik, K. & Tchórzewska-Cieślak, B. (2014). Water Supply System operation regarding consumer safety using Kohonen neural network; in: Safety, Reliability and Risk Analysis: Beyond the Horizon - Steenbergen et al. (Eds), Taylor & Francis Group, London: 1115-1120.
  • [18] Rak, J.R. (2015). Propozycja oceny dywersyfikacji objętości wody w sieciowych zbiornikach wodociągowych, Czasopismo Inżynierii Lądowej, Środowiska i Architektury, JCEEA, t. XXXII, z. 62 (1/15), s. 339-349. DOI:10.7862/rb.2015.23
  • [19] Rak, J., Pietrucha-Urbanik, K. (2015). New directions for the protection and evolution of water supply systems - smart water supply. Czasopismo Inżynierii Lądowej, Środowiska i Architektury - Journal of Civil Engineering, Environment And Architecture. JCEEA, z. 62 (3/I/2015), pp. 365-373. DOI: 10.7862/rb.2015.121
  • [20] Ritter, G. & Gallegos, T. (2002). Bayesian object identification: variants. Journal of Multivariate Analysis 81: 301-334.
  • [21] Tchórzewska-Cieślak, B. (2008). Niezawodność i bezpieczeństwo systemów komunalnych. Oficyna Wydawnicza Politechniki Rzeszowskiej, Rzeszów.
  • [22] Tchórzewska-Cieślak, B. (2014). Bayesian model of urban water safety management. Global NEST Journal, Vol 16, No 4, pp 667-675.
  • [23] Tchórzewska-Cieślak, B. & Pietrucha-Urbanik, K. (2015). Risk management in water distribution system operation and maintenance using Bayesian theory. Progress in Environmental Engineering - Tomaszek and Koszelnik (eds.). Taylor & Francis Group, London.
  • [24] Tchórzewska-Cieślak, B., Pietrucha-Urbanik, K. & Szpak, D. (2016). Developing procedures for hazard identification. Journal of Polish Safety and Reliability Association, Summer Safety and Reliability Seminars, Vol 7, No 1, pp. 209-215.
  • [25] Thompson, W.E. & Springer, M.D. (1972). Bayes analysis of availability for a system consisting of several independent subsystems. IEEE Transactions on Reliability, 21(4), 212218.
  • [26] Zhang, T.L. & Horigome, M. 2001. Availability and reliability of system with dependent components and time-varying failure and repair rates. IEEE Transactions on Reliability. 50(2), 151-158. DOI: 10.1109/24.963122.
  • [27] Zitrou, A., Bedford, T. & Walls, L. 2010. Bayes geometric scaling model for common cause failure rates. Reliability Engineering & System Safety, 95(2): 70-76. DOI: 10.1016/j.ress. 2009.08.002.
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-01cf8ccf-5392-4070-8ab6-d12f026a0b0e
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