Reliability evaluation for complex systems based on interval-valued triangular fuzzy weighted mean and evidence network

Aiming at the problem in obtaining the precise failure rates of components, this paper presents a new reliability evaluation method for complex systems using interval triangular fuzzy subset and evidence network (EN). Specifically, it develops the fault tree model based on failure mode and effects analysis (FMEA) and uses the interval-valued triangular fuzzy weighted mean to express the interval failure rates of components. Furthermore, fuzzy fault tree is mapped into an EN to calculate some reliability parameters. In addition, a possibility-based NSG ranking approach is adopted to rank components and get the critical component, which can be used to provide the basis for system optimization and maintenance decision-making. Finally, a numerical example is given to validate the availability and efficiency of the proposed method.

[1]  A. Gandini,et al.  Importance and sensitivity analysis in assessing system reliability , 1990 .

[2]  D. Vasseur,et al.  International survey on PSA figures of merit , 1999 .

[3]  Christer Carlsson,et al.  On Possibilistic Mean Value and Variance of Fuzzy Numbers , 1999, Fuzzy Sets Syst..

[4]  John D. Andrews,et al.  Birnbaum's measure of component importance for noncoherent systems , 2003, IEEE Trans. Reliab..

[5]  Jon C. Helton,et al.  Sensitivity analysis in conjunction with evidence theory representations of epistemic uncertainty , 2006, Reliab. Eng. Syst. Saf..

[6]  Anatoly Lisnianski,et al.  Extended block diagram method for a multi-state system reliability assessment , 2007, Reliab. Eng. Syst. Saf..

[7]  Luigi Portinale,et al.  Bayesian networks in reliability , 2007, Reliab. Eng. Syst. Saf..

[8]  Liudong Xing,et al.  A Logarithmic Binary Decision Diagram-Based Method for Multistate System Analysis , 2008, IEEE Transactions on Reliability.

[9]  Ajit Srividya,et al.  Dynamic fault tree analysis using Monte Carlo simulation in probabilistic safety assessment , 2009, Reliab. Eng. Syst. Saf..

[10]  Yao Wei-xing.  Comments on methods for ranking interval numbers , 2010 .

[11]  Terje Aven,et al.  On the use of uncertainty importance measures in reliability and risk analysis , 2010, Reliab. Eng. Syst. Saf..

[12]  Yonghong Liu,et al.  Using Bayesian networks in reliability evaluation for subsea blowout preventer control system , 2012, Reliab. Eng. Syst. Saf..

[13]  Yao Cheng-yu.  Reliability Analysis of Multi-state System Based on Fuzzy Bayesian Networks and Application in Hydraulic System , 2012 .

[14]  Dongning Chen,et al.  Reliability Analysis of Multi-state System Based on Fuzzy Bayesian Networks and Application in Hydraulic System , 2012 .

[15]  Christophe Simon,et al.  Dynamic Directed Evidential Networks with Conditional Belief Functions: Application to System Reliability , 2012, IPMU.

[16]  Nima Khakzad,et al.  Risk-based design of process systems using discrete-time Bayesian networks , 2013, Reliab. Eng. Syst. Saf..

[17]  Uday Kumar,et al.  Fuzzy fault tree analysis: a review of concept and application , 2013, Int. J. Syst. Assur. Eng. Manag..

[18]  Mohamed Sallak,et al.  Extended Component Importance Measures Considering Aleatory and Epistemic Uncertainties , 2013, IEEE Transactions on Reliability.

[19]  John C. Lee,et al.  Application of fault tree analysis for customer reliability assessment of a distribution power system , 2013, Reliab. Eng. Syst. Saf..

[20]  Xu Han,et al.  A novel evidence-theory-based reliability analysis method for structures with epistemic uncertainty , 2013 .

[21]  Sohag Kabir,et al.  Reliability Analysis of Dynamic Systems by Translating Temporal Fault Trees into Bayesian Networks , 2014, IMBSA.

[22]  Nima Khakzad,et al.  Application of dynamic Bayesian network to risk analysis of domino effects in chemical infrastructures , 2015, Reliab. Eng. Syst. Saf..

[23]  Yao Rui-p.  The Multi-Attribute Group Decision Making Method Based on Interval Valued Triangular Fuzzy Number , 2015 .

[24]  Jinghui Fan,et al.  Diagnosis strategy for complex systems based on reliability analysis and MA DM under epistemic uncertainty , 2015 .

[25]  Miroslaw J. Skibniewski,et al.  A dynamic Bayesian network based approach to safety decision support in tunnel construction , 2015, Reliab. Eng. Syst. Saf..

[26]  Rong Pan,et al.  A computational Bayesian approach to dependency assessment in system reliability , 2016, Reliab. Eng. Syst. Saf..

[27]  Xiaowei Wang,et al.  Reliability evaluation of a multi-state system based on interval-valued triangular fuzzy Bayesian networks , 2015, International Journal of System Assurance Engineering and Management.

[28]  Weiwen Peng,et al.  Reliability assessment of complex electromechanical systems under epistemic uncertainty , 2016, Reliab. Eng. Syst. Saf..

[29]  Quan Sun,et al.  Reliability analysis for multi-state system based on triangular fuzzy variety subset bayesian networks , 2017 .