An extension to Fuzzy Developed Failure Mode and Effects Analysis (FDFMEA) application for aircraft landing system
Introduction
Nowadays, reliability and safety guarantee have been of increasing concern in Iranian Airlines company (Mahan Airline, 2015). FMEA as an inductive technique in nature can be applied in all aspects of failure analysis and tends to prepare information for risk management procedure (Modarres, 1993). It has been used in many different kinds of industry such as nuclear, chemical, electronics, and mechanical (Liu et al., 2013).
FMEA was initially introduced in the 1940s by the U.S military, which published “MIL–P–1629” as a safety standard in 1949 (US Department of Defence, 1980). This technique was first developed in 1960s contractors for the U.S. National Aeronautics and Space Administration (NASA) due to aerospace industry, then it used to tolerate risk for Apollo mission to analyze failures on mission success (Bowles and Peláez, 1995, Carlson and Carl, 2012).
Over the years, many variations of the conventional FMEA have been carried out. A primary discussion of failure analysis which employs a single matrix to system modeling has been performed by Kara-Zaitri et al. (1992). Later that, Bell studied a stochastic model of FMEA (Bell et al., n.d.). Wang and Ruxton (1996) represent an approach in order to combine the Boolean Representation Method (BRM) and FMEA.
However, along all studies based on conventional FMEA technique, it still imposes many common shortages in order to compute Risk Priority Number (RPN ), which is the product of the Severity (S), Detection (D) and Occurrence (O). The different combination of S, O and D might be found out in the same value of RPN. As an example, three types of failure modes with value of (10, 3, 8); (6, 8, 5) and (4, 6, 10) for S, O and D, respectively, have the same RPN = 240. Different combination causes of the conventional FMEA technique cannot recognize the hidden risk implications. Also, conventional FMEA considers the importance of the elements S, O and D with the same weight which is not effective in practical FMEA study. Besides, RPN value can be generated just 120 of the 1000 numbers from the production of elements. It means that the crisp value of RPN is not continues and there are limited numbers to prioritize the failure modes. Moreover, it is possible the assessors face by many lack of data and ambiguous information in conventional FMEA. For this reason finding the exact value of RPN needs the linguistic expressions including High, Low and Medium (Liu, 2016, Wang et al., 2009).
To eliminate the aforementioned limitations the fuzzy set theory as a computational intelligence has been proposed. The formal introduction of fuzzy set theory is formulized by Prof. Zadeh in the early 1960s. Fuzzy set is a class of objects with degree of membership which is in interval zero and one (Zadeh, 1965). The notable point of fuzzy set is that they are more close to ambiguity which is based on approximation rather than preciseness (Gul and Guneri, 2016, Hong et al., 2016, Markowski and Mannan, 2009, Rajakarunakaran et al., 2015).
Over the years, many applications of Fuzzy Developed FMEA (FDFMEA) have been applied in a verity of engineering fields to eliminate the mentioned drawbacks. Wang et al. (2009) estimate the occurrence probability by using expert opinion based on trapezoidal set and they develop it to the other risk parameters. In parallel way a fuzzy expressions is used by Kahraman et al. (2013) in order to prioritize healthcare issues. Besides, a new model based on (α) level was introduced by Hadi-Vencheh and Aghajani (2013). In this study the failure elements are demonstrated by linguistic expressions. Mandal and Maiti (2014) applied fuzzy set theory for risk assessment with using similarity value and possibility approach. In another study, failure analysis is done by employing fuzzy membership function by Helvacioglu and Ozen (2014) to find out the critical failures during yacht design. Also, Dağsuyu et al. (2016) clarified the utilization conventional and fuzzy FMEA in failure analysis in sterilization plant. Despite all researches, there are rare studies to compare the conventional model and new one as FDFMEA on aircraft landing system.
The purpose of this research is reaching to proper comparison based on reliability analysis between classical FMEA and developed FMEA under the fuzzy environment in aircraft landing system. In order to rank the identified failure modes, four experts who have experience and technical knowledge in this field participated in the study. The rest of paper is ordered as follows. After reviewing the proposed model in details in Section 2, FDFMEA will be applied on air craft landing system as case of study in Section 3. Finally, conclusion part will be explained in Section 4.
Section snippets
The proposed model
The structure of the proposed model is arisen from the set of Fuzzy theory and also the qualitative opinions which are extracted by experts’ judgment for purpose of satisfy the FMEA quantifiable ability. The novel model framework is extended in various steps which are merged from conventional FMEA and new one. First of all, like as all FMEA studies the component and process information should be collected. Secondly, the potential failure modes should be determined. Thirdly, at the same time the
Result
In order to explain the proposed method clearly, the detail of one of the numeric failure mode (A simplest aircraft landing system) is chosen as a case of study, because in recent years many accidents related to landing systems are reported by Iranian airlines (Mahan Airline, 2015).
As it shows in Fig. 3 with pressing GDnB and GupB the gear come down and rise respectively. Switch (S1) send a signal to computer (C) during raising the gear (otherwise a fault signal is sent). In opposite side, when
Conclusion
Failure mode and effects analysis (FMEA) is designed as a powerful tool to measure reliability analysis in widespread engineering fields. In FMEA technique, three elements which are called severity, occurrence and detection used to evaluate the potential failure modes. The risk priority number in classic model is equal to product of crisp value of the elements. However, the limitations of conventional FMEA are recognized during the study. Thus, this paper proposed to assess the risks with a
Acknowledgement
In this study, authors would like to express their gratitude to the Iranian Naftair airline for supporting to represent failure modes which common occur in aircraft landing system with appoint of decision rules, and also to four experts participated in this study because of their invaluable insights and inputs.
References (46)
- et al.
Integrating lean principles and fuzzy bow-tie analysis for risk assessment in chemical industry
J. Loss Prev. Process Ind.
(2014) - et al.
Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis
Reliab. Eng. Syst. Saf.
(1995) - et al.
Classical and fuzzy FMEA risk analysis in a sterilization unit
Comput. Ind. Eng.
(2016) - et al.
A fuzzy multi criteria risk assessment based on decision matrix technique: a case study for aluminum industry
J. Loss Prev. Process Ind.
(2016) - et al.
Fuzzy based failure modes and effect analysis for yacht system design
Ocean Eng.
(2014) - et al.
A fuzzy logic and probabilistic hybrid approach to quantify the uncertainty in layer of protection analysis
J. Loss Prev. Process Ind.
(2016) - et al.
Risk evaluation approaches in failure mode and effects analysis: a literature review
Exp. Syst. Appl.
(2013) - et al.
A FTA-based method for risk decision-making in emergency response
Comput. Oper. Res.
(2014) - et al.
Risk analysis using FMEA: Fuzzy similarity value and possibility theory based approach
Expert Syst. Appl.
(2014) - et al.
Fuzzy logic for piping risk assessment (pfLOPA)
J. Loss Prev. Process Ind.
(2009)
Chaotic dynamics of information processing: the “magic number seven plus-minus two” revisited
Bull. Math. Biol.
A new fuzzy multiple attributive group decision making methodology and its application to propulsion/manoeuvring system selection problem
Eur. J. Oper. Res.
Presenting of failure probability assessment pattern by FTA in Fuzzy logic (case study: Distillation tower unit of oil refinery process)
J. Chem. Heal. Saf.
Safety risk assessment and management-the ESA approach
Reliab. Eng. Syst. Saf.
Applications of fuzzy faulty tree analysis and expert elicitation for evaluation of risks in LPG refuelling station
J. Loss Prev. Process Ind.
Safety barriers analysis of offshore drilling system by employing Fuzzy event tree analysis
Saf. Sci.
Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean
J. Expert Syst. Appl.
Defuzzification of fuzzy intervals
Fuzzy Sets Syst.
Effective FMEAs Achieving Safe, Reliable, and Economical Products and Processes Using Failure Mode and Effects Analysis
Fuzzy Multiple Attribute Decision Making, Lecture Notes in Economics and Mathematical Systems
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