The aim of this paper is introducing a method based on Fuzzy Time-To Failure (FTTF) to improve reliability analysis of complex engineering systems based on fault tree analysis. This method focuses on the quantitative part of fault trees (either static or dynamic) analysis and will compute failure probabilities. FTTF model is developed to estimate the reliability of system and solve aforetime methods problems. The presented FTTF model is able to figure out any construction consist of static and dynamic gates with FTTF distributions integrated on Fuzzy Monte Carlo Simulation (FMCS) techniques to analyzing Possibilistic functions associated with the fuzzy probability distributions for each basic event. Using fuzzy algorithm, gates FTTF are generated, and Top-event TTF evaluated. Some case studies are used to demonstrate the priority of this method in exact evaluation in compared with other solving methods (like: BN, Analytical solution, Markov chain and traditional fuzzy fault tree modeling), but has much less effort while having higher accuracy. Finally, this model is implemented in an Emergency Detection System (EDS) which is a useful system in aerospace and space applications.
ModarresM., KaminskizM., KrivstovV., Realiability Engineering and Risk Analysis: A Practical Guide, CRC press, 1999.
2.
KacprzykJ., OnisawaT., Reliability and safety analyses under fuzziness, Springer, 1995.
3.
KapurK.C., Handbook of Industrial Engineering, Reliability and Maintainability, 2001.
4.
ChiacchioF., CompagnoL., D’UrsoD., MannoG. and TrapaniN., An open-source application to model and solve dynamic fault tree of real industrial systems, Software, Knowledge Information, Industrial Management and Applications (SKIMA), 5th International Conference on IEEE, 2011.
5.
WuB., Reliability Analysis of Dynamic Systems: Efficient Probabilistic Methods and Aerospace Applications, Elsevier Science, 2013.
6.
MarquezD., NeilM. and FentonN., Improved reliability modeling using Bayesian networks and dynamic discretization, System Safety95 (4) (2010), 412–425.
7.
XingL., ShresthaA. and DaiY., Exact combinatorial reliability analysis of dynamic systems with sequence-dependent failures, System Safety96 (10) (2011), 1375–1385.
8.
MerleG., RousselJ.M. and LesageJ.J., Quantitative analysis of dynamic fault trees based on the structure function, Quality and Reliability Engineering International30 (1) (2014), 143–156.
9.
BobbioA., PortinaleL., MinichinoM. and CiancamerlaE., Improving the analysis of dependable systems by mapping fault trees into Bayesian networks, & }(3), System Safety71 (2011), 249–260.
10.
MarquezD., NeilM. and FentonN., Solving dynamic fault trees using a new hybrid bayesian network inference algorithm, Control and Automation, 16th Mediterranean Conference on IEEE, 2008.
11.
YugeT. and YanagiS., Quantitative analysis of a fault tree with priority AND gates, System Safety93 (11) (2008), 1577–1583.
12.
BobbioA. and RaiteriD.C., Parametric fault trees with dynamic gates and repair boxes, Reliability and Maintainability, Annual Symposium-RAMS on IEEE, 2004.
13.
DuganJ.B., BavusoS.J. and BoydM.A., Fault trees and Markov models for reliability analysis of fault tolerant systems, Reliability Engineering and System Safety39 (1993), 291–307.
14.
DuganJ.B. and DoyleS.A., New results in fault-tree analysis, Maintainability Symposium, Philadelphia, PA, 1997.
15.
GulatiR. and DuganJ.B., A modular approach for analyzing static and dynamic fault trees, Reliability and Maintainability Symposium Proceedings on IEEE, 1997.
16.
MisraK.B., Handbook of performability engineering, Springer, 2008.
17.
DuganB., BavusoS.J. and BoydM.A., Dynamic fault-tree models for fault-tolerant computer systems, IEEE Transactions on Reliability41 (3) (1992), 363–377.
18.
RaoK.D., RaoV.S., VermaA., SrividyaA., Dynamic fault tree analysis: Simulation approach. Simulation Methods for Reliability and Availability of Complex SystemsSpringer (2010), pp. 41–64.
19.
LiY.F., HuAngH.Z., LiuY. and LiH., A new fault tree analysis method: Fuzzy dynamic fault tree analysis, Eksploatacja I Niezawodnosc14 (3) (2012).
20.
ZadehL.A., Fuzzy sets, Information and control8 (3) (1965), 338–353.
21.
TanakaH., FanL., LaiF. and ToguchiK., Fault-tree analysis by fuzzy probability, IEEE Transactions On Reliability32 (5) (1983), 453–457.
22.
SingerD., A fuzzy set approach to fault tree and reliability analysis, Fuzzy sets and systems34 (2) (1990), 145–155.
23.
WangD. and
TrivediK.S., Computing steady-state mean time to failure for non-coherent repairable systems, IEEE Transactions on Reliability54 (3) (2005), 506–516.
24.
RaoS. and SawyerJ.P., Fuzzy finite element approach for analysis of imprecisely defined systems, AIAA Journal33 (12) (1995), 2364–2370.
25.
ZonouzS.A., and MiremadiS.G., A fuzzy-monte carlo simulation approach for fault tree analysis, Reliability and Maintainability Symposium, 2006 RAMS’06 Annual, 2006.
26.
NadjafiM., FarsiM.A. and NajafiA., Static fault tree analysis using fuzzy-monte carlo simulation,, Tehran, Iran, 14th conference of Iranian aerospace society, 2015.
27.
NadjafiM., FarsiM.A. and JabbariH., Reliability analysis of dynamic fault tree using monte carlo simulation, 23rd Annual international Mechanical Engineering Conference, ISME2015, Amir Kabir University of Technology, Tehran, Iran, 2015.
28.
NadjafiM., FarsiM.A. and NajafiA., Uncertainty improving in importance sampling: An integrated approach with Fuzzy-Cluster sampling, 24th annual European Safety and Reliability (ESREL) Conference, Wroclaw, Poland, 2014.
29.
NadjafiM., FarsiM.A. and JabbariH., Dynamic fault tree analysis based-on monte carol simulation using fuzzy failure distributions, International conference of advances in reliability, maintanance and safety ICRESH-ARMS 2015, Lulea sweden, 2015.
30.
FarsiM.A. and NajafiM., Reliability estimation of multi-state system based on fault tree analysis, Modares Mechanical Engineering15 (1) (2015), 257–266, (In Persian).
31.
NadjafiM., FarsiM.A. and JabbariH., Reliability analysis of multi-state emergency detection system using simulation approach based on fuzzy failure rate, International Journal of System Assurance Engineering and Management, ISSN 0975-6809, Springer (2016), 1–10.
32.
BergG., Monte Carlo sampling of Dynamic Fault Trees for reliability prediction, referaat.cs. utwente.nl, 2008.
33.
MahmoodY., AhmadiA., VermaA.K., SrividyaA. and KumarU., Fuzzy fault tree analysis: A review of concept and application, International Journal of System Assurance Engineering and Management4 (1) (2013), 19–32.
34.
MannoG., ChiacchioF., Compagno D’UrsoL., TrapaniN.,MatCarloRe: An integrated FT and Monte Carlo Simulink tool for the reliability assessment of dynamic fault tree, Expert Systems with Applications39 (12) (2012), 10334–10342.
35.
ChenS.H., WangC.C. and ChangS.M., Arithmetical operations of exponential fuzzy numbers using the Function Principle, New Mathematics and Natural Computation2 (02) (2006), 131–138.
36.
MarseguerraM., ZioE., DevooghtJ. and LabeauP.E., A concept paper on dynamic reliability via Monte Carlosimulation, Mathematics and Computers in Simulation47 (2) (1998), 371–382.
37.
ZioE., Reliability engineering: Old problems and new challenges, Reliability Engineering & System Safety94 (2) (2009), 125–141.
38.
DuboisD., PradeH.M., FarrenyH., MartinC.R., and TestemaleC., Possibility theory: An approach to computerized processing of uncertainty, Plenum press, New York, 1988.
39.
ZioE., PedroniN., Reliability estimation by advanced Monte Carlo simulation. Simulation Methods for Reliability and Availability of Complex SystemsSpringer, 2010, pp. 3–39.
40.
FlageR., BaraldiP., ZioE. and AvenT., Probability and possibility-based representations of uncertainty in fault tree analysis, Risk Analysis33 (1) (2013), 121–133.
41.
NadjafiM., FarsiM.A. and JabbariH., Dynamic fault tree analysis using fuzzy L-U bounds failure distributions, Journal of Intelligent & Fuzzy Systems6 (2017), 3275–3286, IOS Press.
