Proceedings of the Thirty-second (2022) International Ocean and Polar Engineering Conference
Shanghai, China, June 5–10, 2022
Copyright © 2022 by the International Society of Offshore and Polar Engineers (ISOPE)
ISBN 978-1-880653-81-4; ISSN 1098-6189

www.isope.org

An integrated approach based on interval 2-tuple linguistic variables for risk analysis
Fang Hana,b, Yang Yua, Hao Wangba State Key Laboratory of Hydraulic Engineering Simulation and Safety, School of Civil Engineering, Tianjin University, Tianjin, Chinab China Institute of Marine Technology & Economic (CIMTEC), Peking, China

capability of FMEA, many studies have been conducted.
Risk priorities of failure modes in FMEA is a complicated process
considering taking into account multiple risk factors. Hence, thedetermination of risk ranking in FMEA is essentially regard as a multiple
criteria decision-making (MCDM) process (Franceschini F and GalettoM, 2001). The MCDM techniques can support to solve FMEA problems
including gray relation analysis (GRA) (Nie W et al. 2019), analytic
hierarchy process (AHP) (Ikua BW and Sakthivel G, 2019), analytic
network process (ANP) (Rahmatin N et al. 2018), a technique for order
preference by similarity (TOPSIS) (Hu Y P et al. 2018), best worst
method (BWM) (Ghoushchi SJ et al. 2019), decision-making trial andevaluation laboratory (DEMATEL) method (Seyed SM et al. 2006) multi
attributive idea real comparative analysis (MAIRCA) method (Boral S etal. 2020) and others (Nie W, 2019; Wang W et al. 2019b; Wen TC et al.
2020). In contrast, The MAIRCA method, recently developed by theCentre for Logistic Research of Defence University of Belgrade, is acomparative model between ideal and actual alternatives (failure modes)
taking into consideration multiple criterions (risk factors). It has been
proven to more stable than other usual MCDM methods, like VIKOR orTOPSIS. In addition, it can deal with lots of indicators and elements
without giant computation. Thus, many pieces of research involving
MAIRCA method have been carried out. For example, Pamucar D et al.
(2017) proposed the hybrid DEMTATEL-ANP-MAIRCA model basedon interval rough numbers to solve MCDM problem. Gul M and Ak MF(2020) considered a risk evaluation model by two MCDM models, which
are the best and worst method (BWM) and MAIRCA method to prioritize
hazards. Soumava Borala et al. (2019) developed an integrated MCDM
approach by combining fuzzy AHP and fuzzy MAIRCA to improve
FMEA and overcome its several shortcomings.
Significantly, proper determination of risk factor weights plays animportant role in FMEA, because alteration in the weight values may
affect the final risk priorities. In this aspect, Sangaiah AK et al. (2015)
implemented DEMATEL method to determine the risk factor weights.
DEMATEL method is a comprehensive method presented by the Geneva
Research Centre of the Battelle Memorial Institute (Fontela E and GabusA, 1974) for building and analyzing the structure model of complicatedcausal relationships. It employs matrices and diagraphs for visualizing
complex structures and is able to determine the weight of the elements
analyzed. Since its characteristics and capabilities, DEMATEL method

ABSTRACT
Failure mode and effect analysis (FMEA) is a prospective and systematic
technique for risk analysis. Nevertheless, the traditional FMEA presents
several shortcomings regarding risk ranking, risk factor weights andassessment information expression, etc. In this study, an integrated risk
analysis approach based on interval 2-tuple linguistic variables isproposed to improve the performance of FMEA. Moreover, decisionmaking trial and evaluation laboratory (DEMATEL) method and multi
attributive idea real comparative analysis (MAIRCA) method are applied
to obtain risk factor weights and risk priority rankings respectively.
Finally, a case is provided to demonstrate the accuracy and practicalityof the proposed method.

KEYWORDS: risk analysis; failure mode and effect analysis (FMEA);
interval 2-tuple linguistic variables; decision-making trial and evaluationlaboratory (DEMATEL); multi attributive idea real comparative analysis(MAIRCA).

INTRODUCTION
Failure mode and effect analysis (FMEA) has proven to be an attractive
risk analysis tool, which has been widely applied to identify the potential
failures of process, service or system and analysis the cause and effectsof these failures. The main purpose of the FMEA technique is to rank thefailure modes in terms of a risk priority number (RPN). However, it hasbeen extensively criticized for existing many shortcomings (Huang J etal. 2017; Li Z and Chen L, 2019; Fattahi R et al. 2018; Zhou Q and ThaiVV, 2016). The major ones are as follows: (1) different combinations ofrisk factors may lead to the same value of RPN, however, their hidden
implications may be completely different. (2) the relative importance of
three factors, which are occurrence(O), severity(S) and detection(D), is
not taken into account. The three risk factors are treated with the sameweight, which may not be the case when considering a practicalapplication of FMEA. (3) in many cases, decision makers prefer to use
qualitative linguistic variables, such as low, high, likely ,etc. to express
own ambiguous and vague assessments. Hence, the current scales from
1 to 10 may not correspond to the practical situation. To address theseshortcomings of the traditional FMEA method and promote the analysis

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