Abstract
The methods of identification of the operation processes of complex technical systems are presented. These are methods and procedures for estimating the unknown basic parameters of the system operation process, semi-Markov model and identifying the distributions of the conditional system operation process sojourn times at the operation states. The formulae estimating the probabilities of the system operation process straying at the operation states at the initial moment, the probabilities of the system operation process transitions between the operation states and the parameters of the distributions suitable and typical for the description of the system operation process conditional sojourn times at the operation states are given. Namely, the parameters of the uniform distribution, the triangular distribution, the double trapezium distribution, the quasi-trapezium distribution, the exponential distribution, the Weibull’s distribution and the chimney distribution are estimated using the statistical methods such as the method of moments and the maximum likelihood method. The chi-square goodness-of-fit test is described and proposed to be applied for verifying the hypotheses about these distributions, choice validity. The procedure of statistical data sets, uniformity analysis based on Kolmogorov-Smirnov test is proposed to be applied to the empirical conditional sojourn times at the operation states coming from different realizations of the same complex technical system operation process. The applications of the proposed statistical methods of the unknown parameters, identification of the complex technical system operation process model for determining the operation parameters of the exemplary system, the port oil transportartion system and the maritime ferry technical system are presented. The procedure of testing the uniformity of statistical data sets is applied to the realizations of the conditional sojourn times at the operation states of the ferry technical system collected at two different operating conditions.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Barbu V, Limnios N (2006) Empirical estimation for discrete-time semi-Markov processes with applications in reliability. J Nonparametric Stat 18(7–8):7–8 483-498
Collet J (1996) Some remarks on rare-event approximation. IEEE Trans Reliab 45:106–108
Gamiz ML, Roman Y (2008) Non-parametric estimation of the availability in a general repairable. Reliab Eng Syst Saf 93(8):1188–1196
Giudici P, Figini S (2009) Applied data mining for business and industry. Wiley, London
Habibullah MS, Lumanpauw E, Kolowrocki K, Soszynska J, Ming NG (2009) A computational tool for general model of operation processes in industrial systems operation processes. Electron J Reliab Risk Anal: Theory Appl 2(4):181–191
Helvacioglu S, Insel M (2008) Expert system applications in marine technologies. Ocean Eng 35(11–12):1067–1074
Hryniewicz O (1995) Lifetime tests for imprecise data and fuzzy reliability requirements. In: Onisawa T, Kacprzyk J (eds) Reliability and safety analyses under fuzziness. Physica Verlag, Heidelberg, pp 169–182
Kołowrocki K (2004) Reliability of large systems. Elsevier, Amsterdam
Kołowrocki K, Soszyńska J (2008) A general model of industrial systems operation processes related to their environment and infrastructure. Summer Safety & Reliability Seminars. J Pol Saf Reliab Assoc 2(2):223–226
Kolowrocki K, Soszynska J (2009) Modeling environment and infrastructure influence on reliability and operation process of port oil transportation system. Electron J Reliab Risk Anal: Theory Appl 2(3):131–142
Kolowrocki K, Soszynska J (2009) Safety and risk evaluation of Stena Baltica ferry in variable operation conditions. Electron J Reliab Risk Anal: Theory Appl 2(4):68–180
Kołowrocki K, Soszyńska J (2009) Statistical identification and prediction of the port oil pipeline system’s operation process and its reliability and risk evaluation. Summer Safety & Reliability Seminars. J Pol Saf Reliab Assoc 3(2):241–250
Kołowrocki K, Soszyńska J (2009) Methods and algorithms for evaluating unknown parameters of operation processes of complex technical systems. Summer Safety & Reliability Seminars. J Pol Saf Reliab Assoc 3(1–2):211–222
Kołowrocki K, Soszyńska J (2009) Methods and algorithms for evaluating unknown parameters of components reliability of complex technical systems. Summer Safety & Reliability Seminars. J Pol Saf Reliab Assoc 4(2):223–230
Kołowrocki K, Soszyńska J (2009) Statistical identification and prediction of the port oil pipeline system’s operation process and its reliability and risk evaluation. Summer Safety & Reliability Seminars. J Pol Saf Reliab Assoc 4(2):241–250
Kolowrocki K, Soszynska J (2010) Reliability modeling of a port oil transportation system’s operation processes. Int J of Perform Eng 6(1):77–87
Kolowrocki K, Soszynska J (2010) Reliability, availability and safety of complex technical systems: modelling–identification–prediction–optimization. Summer Safety & Reliability Seminars. J Pol Saf Reliab Assoc 4(1):133–158
Kolowrocki K, Soszynska J (2010) Testing uniformity of statistical data sets coming from complex systems operation processes. Summer Safety & Reliability Seminars. J Pol Saf Reliab Assoc 4(1):123–132
Limnios N, Oprisan G (2005) Semi-Markov processes and reliability. Birkhauser, Boston
Limnios N, Ouhbi B, Sadek A (2005) Empirical estimator of stationary distribution for semi-Markov processes. Commun Stat-Theory Methods 34(4):987–995 12
Macci C (2008) Large deviations for empirical estimators of the stationary distribution of a semi-Markov process with finite state space. Commun Stat-Theory Methods 37(19):3077–3089
Mercier S (2008) Numerical bounds for semi-Markovian quantities and application to reliability. Methodol Comput Appl Probab 10(2):179–198
Rice JA (2007) Mathematical statistics and data analysis. Duxbury. Thomson Brooks/Cole. University of California, Berkeley
Soszyńska J (2007) Systems reliability analysis in variable operation conditions. PhD thesis, Gdynia Maritime University-System Research Institute Warsaw (in Polish)
Soszyńska J, Kołowrocki K, Blokus-Roszkowska A, Guze S (2010) Identification of complex technical system components safety models. Summer Safety & Reliability Seminars. J Pol Saf Reliab Assoc 4(2):399–496
Vercellis S (2009) Data mining and optimization for decision making. Wiley, Indianapolis
Wilson AG, Graves TL, Hamada MS et al (2006) Advances in data combination, analysis and collection for system reliability assessment. Stat Sci 21(4):514–531
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2011 Springer-Verlag London Limited
About this chapter
Cite this chapter
Kołowrocki, K., Soszyńska-Budny, J. (2011). Complex Technical System Operation Processes Identification. In: Reliability and Safety of Complex Technical Systems and Processes. Springer Series in Reliability Engineering. Springer, London. https://doi.org/10.1007/978-0-85729-694-8_4
Download citation
DOI: https://doi.org/10.1007/978-0-85729-694-8_4
Published:
Publisher Name: Springer, London
Print ISBN: 978-0-85729-693-1
Online ISBN: 978-0-85729-694-8
eBook Packages: EngineeringEngineering (R0)