Risk evaluation in failure mode and effects analysis of aircraft turbine rotor blades using Dempster–Shafer evidence theory under uncertainty

Yang, Jianping; Huang, Hong Zhong; Li, He; Zhu, Shibai; Wen, Dunwei

doi:10.1016/j.engfailanal.2011.06.014

Cited by 182 publications

(178 citation statements)

References 17 publications

Supporting

Mentioning

156

Contrasting

Unclassified

Order By: Relevance

“…Liu et al(2011) and Liu et al(2012) explained several shortcomings such as possible same RPN values for different risk implications, ignored differency of relative importance among the three risk factors, possible misleading due to mathematical product of meaningless ordinal numbers, and etc. Similar drawbacks are pointed out by many authors like Chin et al(2009aChin et al( , 2009b, Chang and Sun(2009), Abdelgawad and Fayek(2010), , Tay and Lim(2010), Cheng(2010, 2011), Zhang and Chu(2011), Gargama and Chaturvedi(2011), Zammori and Gabbrielli(2011), Yang et al(2011), Kutlu and Ekmekcioglu(2012), Xiao et al(2011) and so on. The frequently mentioned limitations may be summarized as (i) unrealistic assumption of equally weighted RPN elements, (ii) same RPN values possible even with totally different risk context, (iii) possible inconsistent rating among FMEA team members, and (iv) lack of scientific basis for RPN calculation.…”

Section: Introductionmentioning

confidence: 79%

A Modified Metric of FMEA for Risk Evaluation Based on ASIL of Safety System

백명식¹,

장현애

권혁무³

2014

Journal of the Korean society for quality management

View full text Add to dashboard Cite

Purpose: The purpose of this study is to suggest a modified approach that compensates some shortcomings of RPN with relevant strength of ASIL for Safety System and suggests systematic and logical approach for FMEA.Methods: By comparing the objectives, determination procedures, and key conceptual differences of RPN and ASIL, a refined method of risk evaluation and a new risk metric are devised.Results: While the traditional FMEA provides only rough evaluation of relative risk for each failure, the proposed method compensates its shortcomings with relevant strength of ASIL and provides a more logical and practical procedure of risk evaluation. Conclusion:The new metric RPM provides not only a comparative priority rank but also the degree of physical seriousness. Besides, it may have even more benefits for various applications if the severity can be expressed as monetary amount of losses.

show abstract

Section: Introductionmentioning

confidence: 79%

A Modified Metric of FMEA for Risk Evaluation Based on ASIL of Safety System

백명식¹,

장현애

권혁무³

2014

Journal of the Korean society for quality management

View full text Add to dashboard Cite

show abstract

“…One is theoretic development related to the fundamentals of reliability theory, e.g. imprecise reliability (Walley, 1991;Utkin and Coolen, 2007;Kozine and Filimonov, 2000) and fuzzy reliability (Cai et al, 1991a;1991b;1993;Huang et al, 2004;; the other is computational (or algorithmic) development in analysis and the design method, e.g., data fusion technology applied to reliability assessment (Hall and Llinas, 1997;Zhang et al, 2010a;Sun et al, 2008;Yang, 2011a; and optimum design methods (Youn and Choi, 2004b;Youn et al, 2004;Aughenbaugh and Paredis, 2005;Huang et al, 2005a;Limbourg, 2005;Mourelatos and Zhou, 2005;Huang et al, 2006a;2012a). These are illustrated in the sections that follow.…”

Section: General Topics Of Applicationsmentioning

confidence: 99%

“…In the area of reliability engineering, reliability estimation and design are investigated by (Mourelatos and Zhou, 2004), Kozine and Filimonov (2000); Moller et al (1999;Huang (1995;; Huang et al (2006a;; Huang (2012); Li et al (2012); Pang et al (2012); Wang et al (2011;2012) and Xiao et al (2012). Modeling of reliability using a new data fusion rule is proposed by Delmotte and Borne (1998); Sun et al (2008) and Yang et al (2011a); Possibility-based design optimization is studied and developed by (Mourelatos and Zhou, 2004;Youn, 2005;Youn and Choi, 2004a;Youn et al, 2004;Choi et al, 2004;2012b;Zhang et al, 2010b). Fuzzy reliability theory in the context of possibility theory is proposed and developed by (Cai et al, 1991a;1991b;1993;Utkin and Gurov, 1996;Onisawa, 1988;Huang et al, 2004;.…”

Section: General Topics Of Applicationsmentioning

confidence: 99%

“…As a more general tool for uncertainty analysis, evidence theory has also been applied to many areas, including artificial intelligence (particularly in the development of expert systems) (Bae et al, 2004b;Nikolaidis and Haftka, 2001), object detection and approximate reasoning (Lowrance et al, 1986;Perrin et al, 2004;Xu and Smets, 1996;Borotschnig et al, 1999), design optimization (Mourelatos and Zhou, 2005), multidisciplinary design optimization (Agarwal et al, 2004), uncertainty quantification (Bae et al, 2004a;, risk and reliability evaluation (Yang et al, 2011b), remote sensing classification (Lee et al, 1987), pattern recognition and image analysis, decision making (Buckley, 1988;Limbourg, 2005), data fusion (Delmotte and Borne, 1998;Hall and Llinas, 1997;Sun et al, 2008;Yang et al, 2011a) and fault diagnosis (Fan and Zuo, 2006a;Wu et al, 1990). The popularity of evidence theory has risen, however, because evidence theory requires epistemological assumptions that are at odds with those underlying classical and Bayesian probability theories (Fioretti, 2004).…”

Section: General Topics Of Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

Possibility and Evidence-Based Reliability Analysis and Design Optimization

Huang

Liu

et al. 2013

American Journal of Engineering and Applied Sciences

View full text Add to dashboard Cite

Engineering design under uncertainty has gained considerable attention in recent years. A great multitude of new design optimization methodologies and reliability analysis approaches are put forth with the aim of accommodating various uncertainties. Uncertainties in practical engineering applications are commonly classified into two categories, i.e., aleatory uncertainty and epistemic uncertainty. Aleatory uncertainty arises because of unpredictable variation in the performance and processes of systems, it is irreducible even adding more data or knowledge. On the other hand, epistemic uncertainty stems from lack of knowledge of the system due to limited data, measurement limitations, or simplified approximations in modeling system behavior and it can be reduced by obtaining more data or knowledge. More specifically, aleatory uncertainty is naturally represented by a statistical distribution and its associated parameters can be characterized by sufficient data. If, however, the data is limited and can be quantified in a statistical sense, epistemic uncertainty can be considered as an alternative tool in such a situation. Of the several optional treatments for epistemic uncertainty, possibility theory and evidence theory have proved to be the most computationally efficient and stable for reliability analysis and engineering design optimization. This study first attempts to provide a better understanding of uncertainty in engineering design by giving a comprehensive overview of its classifications, theories and design considerations. Then a review is conducted of general topics such as the foundations and applications of possibility theory and evidence theory. This overview includes the most recent results from theoretical research, computational developments and performance improvement of possibility theory and evidence theory with an emphasis on revealing the capability and characteristics of quantifying uncertainty from different perspectives. Possibility and evidence theory-based reliability methods have many advantages for practical engineering when compared with traditional probability-based reliability methods. They can work well under limited data while the latter need large amounts of information, more than possible in engineering practice due to aleatory and epistemic uncertainties. The possible directions for future work are summarized.

show abstract

“…Structural safety assessments of building collapse in the context of reliability have been documented in Raphael et al (2011). Risk evaluation of failure modes for turbine rotor blades based on Dempster Schafer's theory have been attempted in Yang et al (2011). Stochastic and quantitative risk assessment techniques have been used in Marhavilas and Koulouriotis (2011) to model the worksites of an electric power provider.…”

Section: Introductionmentioning

confidence: 99%

A systems based approach for financial risk modelling and optimisation of the mineral processing and metal production industry

Pan

Korre

Durucan

2016

Computers & Chemical Engineering

View full text Add to dashboard Cite

Large scale engineering process systems are subject to a variety of risks which affect the productivity and profitability of the industry in the long run. This paper outlines the short comings of the current methods of risk quantification and proposes a systems engineering framework to overcome these issues. The functionality of the developed model is illustrated for the case of mineral processing and metal production industries using a copper ore processing and refined metal production case study. The methodology provides a quantitative assessment of the risk factors and allows the opportunity to minimise financial losses, which would help investors, insurers and plant operators in these sectors to make appropriate risk hedging policies. The models developed can also be coupled with evolutionary or swarm based algorithms for optimising the systems. A numerical example is illustrated to demonstrate the validity of the proposition.

show abstract

Risk evaluation in failure mode and effects analysis of aircraft turbine rotor blades using Dempster–Shafer evidence theory under uncertainty

Cited by 182 publications

References 17 publications

A Modified Metric of FMEA for Risk Evaluation Based on ASIL of Safety System

A Modified Metric of FMEA for Risk Evaluation Based on ASIL of Safety System

Possibility and Evidence-Based Reliability Analysis and Design Optimization

A systems based approach for financial risk modelling and optimisation of the mineral processing and metal production industry

Contact Info

Product

Resources

About