A Modified Model of Failure Mode and Effects Analysis Based on Generalized Evidence Theory

Zhou, Deyun; Tang, Yongchuan; Jiang, Wen

doi:https://doi.org/10.1155/2016/4512383

Mathematical Problems in Engineering

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Uncertainty Management in Smart Environment

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Research Article | Open Access

Volume 2016 | Article ID 4512383 | https://doi.org/10.1155/2016/4512383

A Modified Model of Failure Mode and Effects Analysis Based on Generalized Evidence Theory

Deyun Zhou,¹Yongchuan Tang,^1,2and Wen Jiang¹

Academic Editor: Jianbing Ma

Received28 Apr 2016

Revised15 Jun 2016

Accepted30 Jun 2016

Published17 Aug 2016

Abstract

Due to the incomplete knowledge, how to handle the uncertain risk factors in failure mode and effects analysis (FMEA) is still an open issue. This paper proposes a new generalized evidential FMEA (GEFMEA) model to handle the uncertain risk factor, which may not be included in the conventional FMEA model. In GEFMEA, not only the conventional risk factors, the occurrence, severity, and detectability of the failure mode, but also the other incomplete risk factors are taken into consideration. In addition, the relative importance among all these risk factors is well addressed in the proposed method. GEFMEA is based on the generalized evidence theory, which is efficient in handling incomplete information in the open world. The efficiency and some merit of the proposed method are verified by the numerical example and a real case study on aircraft turbine rotor blades.

1. Introduction

Failure mode and effects analysis (FMEA) starts from aerospace industry in 1960s. It is a structural tool for analyzing the potential failure modes in a process, designing activity, service process, and so on. Based on the empirical knowledge from the FMEA team members, each failure mode will get a risk priority number to define its risk level, as well as some suggestions on how to control these failure modes to prevent them from having a bad effect on the customers. FMEA is an effective preventive approach to reduce the possibility of a failure. So far, FMEA has become a useful method in risk analysis being widely used in many real applications, like nuclear safety systems [1], software engineering [2], complex system analysis [3, 4], medical management [5–7], patient safety evaluation [8], shipping equipment [9, 10], automotive industry [11], food industry [12], and so on [13–17]. Currently, the study corresponding to FMEA mainly focuses on the following aspects.(i)Applying FMEA approach to many more particular fields for risk analysis: except for those applications mentioned above, FMEA is also used as a risk assessment tool in other particular fields like agriculture and food domain [18], environment protection [19], and so on [20, 21].(ii)Modifying the conventional risk priority number (RPN) model, which is the product of the three risks factors, occurrence (), severity (), and detection (), to make it more rational for ranking the priority of failure modes: the modified RPN value is based on many theories like the fuzzy set theory [16, 22, 23], the grey theory [24], the Monte Carlo method [25], the evidence theory [23, 26, 27], and so on [28–30]. Some of the proposed methods are hybrid methods [14, 23, 31]. A more detailed literature review on this topic is studied by Liu et al. [32].(iii)Addressing the subjective risk evaluation information of FMEA more flexibly: the evaluation information in FMEA method is effective and more flexible while it is combined with the approach of fuzzy theory [33–35], the grey theory [36, 37], the D-S evidence theory [23, 38, 39], the TOPSIS method [14, 40], the OWA operator [41], the D numbers [36], the AHP/ANP method [31, 42], the Bayesian reasoning method [28], and so on [5].These studies mentioned above all make contribution to improve the conventional FMEA method or extend it to different particular fields as an efficient risk analysis tool. But little attention has been paid to the incomplete risk factor; in other words, the other uncertain risk factor except for , , and should also be taken into consideration in real application. Other risk factors may be the period of development, the cost [30], the uncertain risk factors from many suppliers domestic and external, and so on. For example, from the commercial perspective, the cost can be the key factor in terms of a company’s financial objectives [30, 43]. And these risk factors should be taken into consideration independently like , , and . So, how to model these incomplete risk factors out of the conventional FMEA is addressed in this paper; then a modified model of FMEA is proposed based on the generalized evidence theory (GET) [44], namely, generalized evidential FMEA (GEFMEA). In addition, the relative importance among all these risk factors, not only the conventional one in RPN, but also the uncertain one, is taken into consideration in GEFMEA.

The generalized evidence theory [44], which is a more generalized situation of the Dempster-Shafer evidence theory (D-S evidence theory) [45, 46], is developed to handle the uncertain information in the open world. D-S evidence theory has been studied extensively during the past decades [47]; it is a useful mathematical theory for information fusion in real applications [26, 48–50]. Some key problems in D-S evidence theory are still worth further study, for example, the dependent evidence combination [51] and the determination of basic probability assignment [52]. The generalized evidence theory inherits the advantages of D-S evidence theory; what is more, if the frame of discernment is incomplete, the generalized basic probability assignment (GBPA) and generalized combination rule (GCR) in GET can handle the incomplete knowledge more efficiently [44]. In this paper, the incomplete risk factor which comes from the incomplete frame of discernment of FMEA in the open world is expressed by the empty set in the frame of generalized evidence theory. The GBPA is used to handle the relative importance of all these risk factors, including the incomplete one. In this way, the proposed GEFMEA model seemed as a more generalized model in the open world extended from conventional FMEA. GEFMEA can be degenerated to the conventional FMEA whenever it is necessary.

The rest of this paper is organized as follows. In Section 2, some preliminaries are briefly introduced. In Section 3, a new generalized evidential FMEA (GEFMEA) model is proposed. Two experiments based on GEFMEA are shown in Section 4. The conclusions are given in Section 5.

2. Preliminaries

In this section, some preliminaries are introduced, including the failure mode and effects analysis (FMEA) model [54], D-S evidence theory [45, 46], the generalized evidence theory (GET) [44], and the pignistic probability transformation (PPT) model [55].

2.1. Failure Mode and Effects Analysis

FMEA is one of the systematic techniques for risk analysis. Generally, FMEA model includes the following steps [54].

Step 1. Identifying the team: FMEA team members should be with the relevant experience and necessary authority.

Step 2. It includes defining the scope of the FMEA analysis and the customers of the FMEA process.

Step 3. It includes identifying the functions, requirements, and specifications relevant to the defined scope, as well as the potential failure modes, effects, causes, and controls.

Step 4. It includes identifying and assessing risk.

Step 5. It includes defining recommended actions and results.

Among all these five steps, Steps 1–3 are mainly based on empirical knowledge and qualitative analysis. In Step 4, the risk priority number (RPN) offers a useful way to assess the risk level of each failure mode. Step 5 is based on Step 4 and other more empirical knowledge. The risk evaluation in conventional FMEA is determined by the risk priorities of failure modes through the RPN value, which is defined as the product of three risk factors of a failure mode [29]:where is the probability of occurrence of a failure mode, is the severity of a failure effect, and is the probability of a failure being detected. Each risk factor has a numerical rating from 1 to 10. The suggested criterion of rating for occurrence () is shown in Table 1. Similarly, the criteria of rating for severity and detection can be found in [11, 32, 54].

2.2. Dempster-Shafer Evidence Theory

The obtained information in real word is often vague and incomplete [56]. For example, in complex systems, the factors are influenced by each other with very complicated manners, which is hardly to represent with analytic methods [57, 58]. To address this issue, there are many math tools to handle uncertainty [59]. For example, fuzzy set theory is presented to deal with linguistic variables [60–62], logic problem [63, 64], and decision making [65]. On the other hand, as a generalization of the classic probability theory, the D-S theory has an ability to handle uncertainty of imprecision embedded in the evidence, and it has been increasingly applied in many fields, such as decision making [51, 66], fault diagnosis [36, 48], and data fusion [67]. Formally, the evidence theory concerns the following preliminary notations. Some basic concepts of the D-S evidence theory [45, 46] are introduced, including the frame of discernment, the basic probability assignment (BPA), and Dempster’s rule of combination.

Definition 1 (frame of discernment). Assume a finite nonempty set of mutually exclusive events , the set of all subsets of , which is power set , known as the frame of discernment, denoted as [45]

Definition 2 (basic probability assignment). The basic probability assignment function or mass function is defined as a mapping from the power set of to a number between 0 and 1, satisfying [45, 46]where is an empty set, is any subsets of , and the mass function represents how strongly the evidence supports . The mass represents the uncertainty of the evidence.

Definition 3 (Dempster’s rule of combination). The rule of combination combines two BPAs in such a way that the new BPA represents a consensus of the contributing pieces of evidence. Dempster’s rule of combination is the orthogonal sum of and . Dempster’s rule of combination is defined as [45]where is a normalization constant worked as the conflict coefficient of two BPAs, defined as [45]where , , and are subsets of .

For more information about D-S evidence theory, one can refer to [45, 46].

2.3. Generalized Evidence Theory

The basic concepts of generalized evidence theory (GET) [44] are introduced in this section. In GET, the generalized basic probability assignment (GBPA) corresponds to basic probability assignment (BPA) in D-S evidence theory, which is used for data expression and modeling, the generalized combination rule (GCR) is provided for combining conflicting or inconsistent or uncertain body of evidence, and GCR is generalized from Dempster’s rule of combination.

Definition 4. Suppose that is a frame of discernment in the open world. Its power set, , is composed of propositions, ; a mass function is a mapping , satisfying [44]where is the GBPA of the frame of discernment, . The difference between GBPA and BPA is the restriction of . In GET, is not necessary in GBPA. In other words, the empty set can also be a focal element. If , the GBPA degenerates to a conventional BPA in D-S evidence theory.

Definition 5. Given a GBPA , the generalized belief function is , which satisfies [44]

Definition 6. Given a GBPA , the generalized belief function is , which satisfies [44]

Definition 7. In generalized evidence theory, , which means that the intersection between two empty sets is still an empty set. Given two GBPAs ( and ), the generalized combination rule (GCR) is defined as follows [44]:

2.4. Pignistic Probability Transformation

In the transferable belief model (TBM) [55], the pignistic probabilities are typically used for decision making.

Definition 8. Let be a GBPA on the frame of discernment . Its associated pignistic probability transformation (PPT), which represents a point estimate in a belief interval, , is defined as [55]where is the cardinality of subset . The PPT process transforms GBPA to probability distribution.

3. Generalized Evidential FMEA

In real application, conventional FMEA only addresses the three risk factors, occurrence (), severity (), and detection (); all the other potential risk factors are not handled in the conventional FMEA model. In this section, a novel failure mode and effects analysis model named generalized evidential FMEA (GEFMEA) is proposed. GEFMEA is a more generalized FMEA model; it is based on generalized evidence theory. The other risk factors in the open world can be modeled with the empty set in the frame of GET. In addition, with GBPA and PPT, the relative importance among all the risk factors, , , , and the incomplete one, is addressed flexibly.

Each part of the proposed GEFMEA model is presented in this section, including the frame of discernment of GEFMEA, the GBPA of GEFMEA, the generalized evidential risk priority number (GERPN) in GEFMEA, and a flowchart of GEFMEA model.

3.1. Frame of Discernment in GEFMEA

As to the risk factors, the frame of discernment for the conventional FMEA, according to (1), includes the probability of the occurrence of a failure mode (), the severity of a failure effect (), and the probability of a failure to be detected (). Only , , and are in the frame of discernment of conventional FMEA. It only focuses on these three risk factors independently; this is incomplete, because it ignores all the other potential risk factors in real application, such as the period of developing a new safety component for an auto and the cost of the new project. In the open world, the frame of discernment may be incomplete; there are always some uncertain potential risk factors needed to be handled independently. So, the frame of discernment of the risk factors for GEFMEA is defined in this section.

Definition 9. The incomplete frame of discernment for GEFMEA includes the conventional three risk factors: occurrence (), severity (), and detection ():

In Definition 9, the power set of (short for ), , is composed of propositions, including . In GET, can be a focal element; it represents the union of focal elements that are out of the given frame of discernment in the open world. So, in GEFMEA, is used to model all the other uncertain risk factors in the open world due to the incomplete frame of discernment of conventional FMEA. If , the GEFMEA degenerates to conventional FMEA; in this sense, the GEFMEA extends the classical FMEA; GEFMEA is a more generalized model of conventional FMEA.

3.2. GBPA in GEFMEA

For each failure mode, a team member in the FMEA team can assign the relative importance among all the risk factors, , , and by GBPA. This is similar to assigning the risk level for each failure mode from level 1 to level 10 with regard to , , and [26]. So, the relative importance among , , and can be assigned subjectively by each member in the FMEA team according to their prior empirical knowledge. The GBPA for the uncertain risk factors, , can be got from two methods in Definition 11. Definition 10 defines the mass function of each proposition corresponding to risk factors.

Definition 10. For all the propositions in Definition 9, , a mass function is a mapping that satisfieswhere all the possible propositions for may beand the proposition represents the uncertain risk factors.

The GBPA for each proposition in Definition 10, for example, the , , and , is assigned by FMEA team member.

Definition 11. The GBPA for the uncertain risk factors, , may be defined according to one of the two methods. The GBPA for incomplete risk factor, , can be assigned directly by team members subjectively, which is similar to the rating of the three conventional risk factors. The GBPA for incomplete risk factor: if it is not assigned directly by team member, it can be calculated by the following equation:subject towhere .

In this paper, the GBPA for the incomplete risk factors is derived from (14) and (15).

3.3. Generalized Evidential RPN in GEFMEA

In real application, in order to put the limited resources into the key process and reach the financial objectives [43], the risk priority number (RPN) is used to rank and select the failure modes that needed to be handled in advance. In GEFMEA, a generalized evidential RPN, namely, GERPN, is proposed as follows:In GERPN, the incomplete uncertain risk factors are assigned with rating 10, the highest rating. It seems that assigning rating 10 to the uncertain risk factor is a pessimistic strategy. However, it is reasonable since the uncertain risk factors mean quite a high risk level for the consumer. What is more, this is really important to improve the quality of the product.

3.4. GEFMEA Model

The flowchart of the key steps in GEFMEA is shown in Figure 1. According to Figure 1, there are 5 steps to get the priority of all the failure modes, which is described in detail as follows.

Step 1. For each failure mode, each team member gives the subjective relative importance among , , , and the other unknown risk factors by GBPA.

Step 2. For each failure mode, the GBPAs are combined by GCR.

Step 3. For each failure mode, the final weight of each risk factor is derived from combined GBPA by PPT.

Step 4. For each failure mode, the GERPN is calculated, which takes into consideration the relative importance of each risk factor, including the incomplete risk factor.

Step 5. The priority among all the failure modes is got according to GERPNs.

4. Experiments

In this section, some numerical examples and a real case study on aircraft turbine rotor blades are used to illustrate the efficiency of the proposed method. The numerical examples are used to show how the proposed GEFMEA model may be used in industrial application, especially for those cases that incomplete risk factors should be taken into consideration independently. The case study in aircraft turbine rotor blades, which is also compared with the result of conventional D-S evidence theory in the literature [39], shows the compatibility of the proposed method.

4.1. The Illustrative Numerical Example

A few numerical examples of design FMEA (DFMEA) in automotive industry are presented in this section to illustrate the efficiency of the proposed method and its potential application in industrial environment.

4.1.1. Experimental Data

The experimental data to illustrate how the proposed GEFMEA model can be used in real application is empirical data which is proposed based on the process of DFMEA.

Assume that there are five failure modes chosen from the FMEA file for an advanced auto. Each failure mode comes from a different component or system in the auto; the relative importance among these risk factors should be different. What is more, some other risk factors should be taken into consideration, such as the cost and the period of development. The five failure modes (FM) and their conventional RPNs are shown in Table 2.

Three team members (TM1, TM2, and TM3) in a FMEA team assess the relative importance of the risk factors by GBPAs, as shown in Tables 3, 4, 5, 6, and 7, respectively. FM1 is assumed to be a failure mode in a passive safety system, such as a supplemental restraint system (SRS); three members from a FMEA team judge the relative importance of these three risk factors , , and , by GBPAs, as is shown in Table 3. Note that because it is a failure mode for a safety part of an auto, all the GBPAs give a high relative importance to the risk factor . This is reasonable, because it is a matter of lives. What is more, there are some potential factors needed to be taken into consideration, such as the number of loops of the SRS for an auto, the cost, and the period of development of a SRS supplier. Here, the GBPA for uncertain risk factor is not zero. FM2 is assumed to be a failure mode in a power supply system; the GBPAs are shown in Table 4. FM3 is assumed to be a failure mode in an active safety system, such as the head light assembly, which is a key function part at night; the GBPAs are shown in Table 5. FM4 is assumed to be a failure mode in a heating ventilation air conditioning system (HVAC), which should be worked in quite different kinds of environment or even with no need in some area, so the GBPAs sometimes can be very flexible, as is shown in Table 6. FM5 is assumed to be a failure mode in an audio-video entertainment system, which is only for fun, but the cost can range from very cheap to very expensive; its GBPAs are shown in Table 7.

4.1.2. Experiment with GEFMEA Model

According to the proposed method in Section 3, in order to get the ranking of all the failure modes, the GERPN for each failure mode is calculated. The following is the detail of each step to get the GERPN for FM1.

Step 1. The relative importance among , , and in the form of GBPAs is given in Table 3. The GBPA for the uncertain risk factors is calculated by (14) and (15) in Definition 11. For TM1, is calculated as follows:Similarly, for TM2 and TM3 can be calculated. It should be pointed out that all the team members give a high relative importance to the risk factor in the FMEA process since it is a matter of lives for the final consumers.

Step 2. The GBPAs in Table 3 are combined by GCR.
(1) Combine GBPAs from TM1 and TM2 by GCR in Definition 7 with (9):With the result of (18), then the combined result from TM1 and TM2 is as follows:(2) Again, with (9) in Definition 7, we can calculate the result of the combined GBPAs from TM1, TM2, and TM3; the results are as follows:(3) Finally, the GBPAs of FM1 are as follows:

Step 3. The final weight of each risk factor is derived from combined GBPA by PPT.
With (10) in Definition 8, the weight for each risk factor is as follows:

Step 4. The GERPN is calculated, which takes into consideration the relative importance of each risk factor, including the other uncertain risk factors.
According to (16), the GERPN is calculated as follows:

Repeat the four steps above, the GERPNs for FM2, FM3, FM4, and FM5 can be calculated, and the results are shown as (24), (25), (26), and (27), respectively:

4.1.3. Results and Discussions

According to conventional RPN, as is shown in Table 2, the priority among all the five risk factors should be , where “” means “a higher priority than,” while according to the results of GERPNs, the risk priority among these five risk factors is , as is shown in Table 8.

The result of GERPN is compatible with the conventional RPN value in general, which assures that the proposed model is reasonable and effective. The difference between the GERPNs and the RPN hints the influence of the consideration of the relative importance among each risk factor. More importantly, the uncertain risk factors out of the incomplete frame of discernment (, , and ), which may have a great effect on the failure mode analysis, are modeled in GEFMEA. This is distinguished, reflected in the first and third failure modes (FM1 and FM3). Both FM1 and FM3 correspond to safety. As a result, FM1 and FM3 get the higher priority with GERPN. There are two main reasons. One is that other incomplete and uncertain risk factors are taken into consideration by assigning a nonzero mass function to the empty set. As is mentioned above, the incomplete risk factors may be the cost required by different suppliers, the qualified suppliers period of development, and so on. The other reason is that a high relative importance is assigned to the risk factor . The incomplete risk factor and the relative importance of each risk also have an effect on the risk priority of FM4 and FM5.

In order to investigate the influence of the generalized evidential theory, assume that the proposed method is based on classical D-S evidence theory; now (9) of GCR in the Step 2 of GEFMEA approach will be replaced by (4) and (5) of Dempster’s rule of combination. In this case, the other uncertain risk factor, which is represented by empty set in GEFMEA, can no longer be taken into consideration anymore, and the mass of empty set will be redistributed among the three conventional risk factors , , and according to the normalization process with (4) and (5). The value of the RPNs based on classical D-S evidence theory, denoted as ERPN, is presented in Table 9. The risk priority is the same, that is, . The value of ERPN is close to GERPN, sometimes the same as each other if one assesses that there is no other uncertain risk factor in a failure mode, such as in the case FM2. The comparative result shows that if the other risk factor is not taken into consideration even if the FMEA team supports that there exists another risk factor except for , , and , GEFMEA is compatible with the method based on classical D-S evidence theory. In other words, the GEFMEA approach can be degenerated to the approach based on classical D-S evidence theory proposed by Yang et al. [38] once the relative importance among the risk factors is also taken into consideration in [38].

4.2. A Real Case Study on Aircraft Turbine Rotor Blades

A real case study on aircraft turbine rotor blades with the proposed method is studied in this section. Aircraft system is very complicated [68], where the FEMA plays an important role. The experiment result with GEFMEA model is compared with the result of the modified FMEA method based on D-S evidence theory in the literature [38].

4.2.1. Experimental Data

For the 17 failure modes of the aircraft turbine rotor blades in the literature [27, 38, 39], Yang et al. [38], Su et al. [39], and Jiang et al. [27] pay no attention to the relative importance of the three risk factors, , , and , not to say taking into consideration the potential incomplete risk factor. In this paper, in order to use the data in these literatures to show the compatibility of the proposed method, it is assumed that there is no other incomplete risk factor among these 17 failure modes, except for , , and ; the relative importance among the three risk factors is the same, which means 33.33% for , 33.33% for , and 33.33% for .

In the literature [38], according to the assessments from three experts, the combined result of each risk factor is shown in Table 10, as well as the modified RPN noted as MVRPN [38]. (Note that the MVRPN values for FM13 and FM17 are wrong in literature [38]; they are corrected in Table 10 in this paper.)

4.2.2. Experiment with GEFMEA Model

In Section 4.2.1, it is assumed that the relative importance among each risk factor is the same, and there is no other risk factor, which means . So, the GBPAs for each risk factor of each failure mode in Table 10 are the same, shown as follows:With (10) in Definition 8, the weight for each risk factor is as follows:So, the GERPN for the first failure mode in Table 10, according to (16) and (28), is calculated as follows:In this way, the GERPNs of all the 17 failure modes in Table 10 are calculated; the result is in Table 11, as well as the ranking by GERPN and MVRPN.

4.2.3. Results and Discussions

The results in Table 11 show that the priority of each failure mode calculated by GERPN method is the same as it is from MVRPN. This is because the relative importance among each risk factor is not taken into consideration in fact, and there is really no information about other risk factors in the experimental data of the literature [27, 38, 39]. This experiment shows that the GEFMEA is compatible with classical FMEA method. The risk priority based on GERPN and MVRPN [38] will be the same if the FMEA approach is applying in a closed world where there is no incomplete risk factor, and the FMEA team considers that relative importance among each risk factor is the same.

5. Conclusions

In this paper, a generalized evidential FMEA model is proposed based on the generalized evidence theory; the incomplete risk factors apart from , , and are modeled by the empty set in the frame of the generalized evidence theory. The proposed GEFMEA model is a more generalized condition of the classical FMEA model; it can be degenerated to the conventional one if needed. In addition, the relative importance of each risk factor, including the other incomplete risk factor, is addressed in the proposed method. Compared with the other modified FMEA model, the proposed model is simple, which is also a merit in real application. For further study, how to generate the GBPA for GEFMEA intelligently and more flexibly is a challenging and promising problem, and the authors are beginning to work on this topic.

Competing Interests

The authors declare that there are no competing interests regarding the publication of this paper.

Acknowledgments

Thanks are due to Yongrui Tang for his valuable suggestions and discussions on how to revise this work. The work is partially supported by National Natural Science Foundation of China (Grant no. 60904099) and Natural Science Basic Research Plan in Shaanxi Province of China (Program no. 2016JM6018).

References

B. M. Tashjian, “The failure modes and effects analysis as a design tool for nuclear safety systems,” IEEE Transactions on Power Apparatus & Systems, vol. 94, no. 1, pp. 97–103, 1975.
View at: Publisher Site | Google Scholar
D. J. Reifer, “Software failure modes and effects analysis,” IEEE Transactions on Reliability, vol. 28, pp. 247–249, 1979.
View at: Google Scholar
C. E. Peláez and J. B. Bowles, “Using fuzzy cognitive maps as a system model for failure modes and effects analysis,” Information Sciences, vol. 88, no. 1–4, pp. 177–199, 1996.
View at: Publisher Site | Google Scholar
P. G. Hawkins and D. J. Woollons, “Failure modes and effects analysis of complex engineering systems using functional models,” Artificial Intelligence in Engineering, vol. 12, no. 4, pp. 375–397, 1998.
View at: Publisher Site | Google Scholar
J. S. Krouwer, “An improved failure mode effects analysis for hospitals,” Archives of Pathology & Laboratory Medicine, vol. 128, no. 6, pp. 663–667, 2004.
View at: Google Scholar
Q.-L. Lin, D.-J. Wang, W.-G. Lin, and H.-C. Liu, “Human reliability assessment for medical devices based on failure mode and effects analysis and fuzzy linguistic theory,” Safety Science, vol. 62, pp. 248–256, 2014.
View at: Publisher Site | Google Scholar
J. M. Gerhart, H. Spriggs, T. W. Hampton et al., “Applying human factors to develop an improved package design for (Rx) medication drug labels in a pharmacy setting,” Journal of Safety Research, vol. 55, pp. 177–184, 2015.
View at: Publisher Site | Google Scholar
S. Woodhouse, “Engineering for safety: use of failure mode and effects analysis in the laboratory: a well-known engineering tool now being used to assure patient safety,” Laboratory Medicine, vol. 36, pp. 16–18, 2005.
View at: Google Scholar
Q. Zhou and V. V. Thai, “Fuzzy and grey theories in failure mode and effect analysis for tanker equipment failure prediction,” Safety Science, vol. 83, pp. 74–79, 2016.
View at: Publisher Site | Google Scholar
K. Cicek and M. Celik, “Application of failure modes and effects analysis to main engine crankcase explosion failure on-board ship,” Safety Science, vol. 51, no. 1, pp. 6–10, 2013.
View at: Publisher Site | Google Scholar
P. F. Modes, Effects Analysis in Design (Design FMEA) and Potential Failure Modes and Effects Analysis in Manufacturing and Assembly Processes (Process FMEA) Reference Manual, Society of Automobile Engineers, 1994.
L. Kurt and S. Ozilgen, “Failure mode and effect analysis for dairy product manufacturing: practical safety improvement action plan with cases from Turkey,” Safety Science, vol. 55, pp. 195–206, 2013.
View at: Publisher Site | Google Scholar
K. P. Lijesh, S. M. Muzakkir, and H. Hirani, “Failure mode and effect analysis of passive magnetic bearing,” Engineering Failure Analysis, vol. 62, pp. 1–20, 2016.
View at: Publisher Site | Google Scholar
H.-C. Liu, J.-X. You, M.-M. Shan, and L.-N. Shao, “Failure mode and effects analysis using intuitionistic fuzzy hybrid TOPSIS approach,” Soft Computing, vol. 19, no. 4, pp. 1085–1098, 2015.
View at: Publisher Site | Google Scholar
H.-C. Liu, L. Liu, and P. Li, “Failure mode and effects analysis using intuitionistic fuzzy hybrid weighted Euclidean distance operator,” International Journal of Systems Science, vol. 45, no. 10, pp. 2012–2030, 2014.
View at: Publisher Site | Google Scholar | MathSciNet
H.-C. Liu, J.-X. You, X.-Y. You, and M.-M. Shan, “A novel approach for failure mode and effects analysis using combination weighting and fuzzy VIKOR method,” Applied Soft Computing Journal, vol. 28, pp. 579–588, 2015.
View at: Publisher Site | Google Scholar
F. A. Mesa, M. A. S. Hurtado, F. M. S. Margallo, V. G. C. de Vaca, and A. L. Komorowski, “Application of failure mode and effect analysis in laparoscopic colon surgery training,” World Journal of Surgery, vol. 39, no. 2, pp. 536–542, 2015.
View at: Publisher Site | Google Scholar
C. H. Jong, K. M. Tay, and C. P. Lim, “Application of the fuzzy failure mode and effect analysis methodology to edible bird nest processing,” Computers and Electronics in Agriculture, vol. 96, pp. 90–108, 2013.
View at: Publisher Site | Google Scholar
Y.-C. Chen and W.-F. Wu, “Constructing an effective prevention mechanism for MSW lifecycle using failure mode and effects analysis,” Waste Management, vol. 46, pp. 646–652, 2015.
View at: Publisher Site | Google Scholar
S.-H. Luo and G.-G. Lee, “Applying failure mode and effects analysis for successful knowledge management,” Total Quality Management & Business Excellence, vol. 26, pp. 62–75, 2015.
View at: Publisher Site | Google Scholar
A. Petrillo, R. Fusco, V. Granata et al., “Risk management in magnetic resonance: failure mode, effects, and criticality analysis,” BioMed Research International, vol. 2013, Article ID 763186, 5 pages, 2013.
View at: Publisher Site | Google Scholar
Y.-M. Wang, K.-S. Chin, G. K. K. Poon, and J.-B. Yang, “Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean,” Expert Systems with Applications, vol. 36, no. 2, pp. 1195–1207, 2009.
View at: Publisher Site | Google Scholar
J. Guo, “A risk assessment approach for failure mode and effects analysis based on intuitionistic fuzzy sets and evidence theory,” Journal of Intelligent & Fuzzy Systems, vol. 30, no. 2, pp. 869–881, 2016.
View at: Publisher Site | Google Scholar
Y. Geum, Y. Cho, and Y. Park, “A systematic approach for diagnosing service failure: service-specific FMEA and grey relational analysis approach,” Mathematical and Computer Modelling, vol. 54, no. 11-12, pp. 3126–3142, 2011.
View at: Publisher Site | Google Scholar
M. Bevilacqua, M. Braglia, and R. Gabbrielli, “Monte Carlo simulation approach for a modified FMECA in a power plant,” Quality and Reliability Engineering International, vol. 16, no. 4, pp. 313–324, 2000.
View at: Google Scholar
K.-S. Chin, Y.-M. Wang, G. K. K. Poon, and J.-B. Yang, “Failure mode and effects analysis using a group-based evidential reasoning approach,” Computers & Operations Research, vol. 36, no. 6, pp. 1768–1779, 2009.
View at: Publisher Site | Google Scholar
W. Jiang, C. Xie, B. Wei, and D. Zhou, “A modified method for risk evaluation in failure modes and effects analysis of aircraft turbine rotor blades,” Advances in Mechanical Engineering, vol. 8, no. 4, pp. 1–16, 2016.
View at: Publisher Site | Google Scholar
Z. Yang, S. Bonsall, and J. Wang, “Fuzzy rule-based Bayesian reasoning approach for prioritization of failures in FMEA,” IEEE Transactions on Reliability, vol. 57, no. 3, pp. 517–528, 2008.
View at: Publisher Site | Google Scholar
N. Xiao, H.-Z. Huang, Y. Li, L. He, and T. Jin, “Multiple failure modes analysis and weighted risk priority number evaluation in FMEA,” Engineering Failure Analysis, vol. 18, no. 4, pp. 1162–1170, 2011.
View at: Publisher Site | Google Scholar
C. Dong, “Failure mode and effects analysis based on fuzzy utility cost estimation,” International Journal of Quality & Reliability Management, vol. 24, no. 9, pp. 958–971, 2007.
View at: Publisher Site | Google Scholar
M. Abdelgawad and A. R. Fayek, “Risk management in the construction industry using combined fuzzy FMEA and fuzzy AHP,” Journal of Construction Engineering and Management, vol. 136, no. 9, pp. 1028–1036, 2010.
View at: Publisher Site | Google Scholar
H.-C. Liu, L. Liu, and N. Liu, “Risk evaluation approaches in failure mode and effects analysis: a literature review,” Expert Systems with Applications, vol. 40, no. 2, pp. 828–838, 2013.
View at: Publisher Site | Google Scholar
K. Xu, L. C. Tang, M. Xie, S. L. Ho, and M. L. Zhu, “Fuzzy assessment of FMEA for engine systems,” Reliability Engineering and System Safety, vol. 75, no. 1, pp. 17–29, 2002.
View at: Publisher Site | Google Scholar
K.-H. Chang and C.-H. Cheng, “A risk assessment methodology using intuitionistic fuzzy set in FMEA,” International Journal of Systems Science, vol. 41, no. 12, pp. 1457–1471, 2010.
View at: Publisher Site | Google Scholar | MathSciNet
P. A. A. Garcia, R. Schirru, and P. F. Frutuoso E Melo, “A fuzzy data envelopment analysis approach for FMEA,” Progress in Nuclear Energy, vol. 46, no. 3-4, pp. 359–373, 2005.
View at: Publisher Site | Google Scholar
H.-C. Liu, J.-X. You, X.-J. Fan, and Q.-L. Lin, “Failure mode and effects analysis using D numbers and grey relational projection method,” Expert Systems with Applications, vol. 41, no. 10, pp. 4670–4679, 2014.
View at: Publisher Site | Google Scholar
C.-L. Chang, C.-C. Wei, and Y.-H. Lee, “Failure mode and effects analysis using fuzzy method and grey theory,” Kybernetes, vol. 28, no. 8-9, pp. 1072–1080, 1999.
View at: Publisher Site | Google Scholar
J. Yang, H.-Z. Huang, L.-P. He, S.-P. Zhu, and D. Wen, “Risk evaluation in failure mode and effects analysis of aircraft turbine rotor blades using Dempster-Shafer evidence theory under uncertainty,” Engineering Failure Analysis, vol. 18, no. 8, pp. 2084–2092, 2011.
View at: Publisher Site | Google Scholar
X. Su, Y. Deng, S. Mahadevan, and Q. Bao, “An improved method for risk evaluation in failure modes and effects analysis of aircraft engine rotor blades,” Engineering Failure Analysis, vol. 26, pp. 164–174, 2012.
View at: Publisher Site | Google Scholar
A. C. Kutlu and M. Ekmekçioğlu, “Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP,” Expert Systems with Applications, vol. 39, no. 1, pp. 61–67, 2012.
View at: Publisher Site | Google Scholar
K.-H. Chang and C.-H. Cheng, “Evaluating the risk of failure using the fuzzy OWA and DEMATEL method,” Journal of Intelligent Manufacturing, vol. 22, no. 2, pp. 113–129, 2011.
View at: Publisher Site | Google Scholar
F. Zammori and R. Gabbrielli, “ANP/RPN: a multi criteria evaluation of the risk priority number,” Quality and Reliability Engineering International, vol. 28, no. 1, pp. 85–104, 2012.
View at: Publisher Site | Google Scholar
A. Von Ahsen, “Cost-oriented failure mode and effects analysis,” International Journal of Quality & Reliability Management, vol. 25, no. 5, pp. 466–476, 2008.
View at: Publisher Site | Google Scholar
Y. Deng, “Generalized evidence theory,” Applied Intelligence, vol. 43, no. 3, pp. 530–543, 2015.
View at: Publisher Site | Google Scholar
A. P. Dempster, “Upper and lower probabilities induced by a multi-valued mapping,” Annals of Mathematical Statistics, vol. 38, pp. 325–339, 1967.
View at: Publisher Site | Google Scholar | MathSciNet
G. Shafer, A Mathematical Theory of Evidence, Princeton University Press, Princeton, NJ, USA, 1976.
View at: MathSciNet
C. K. Murphy, “Combining belief functions when evidence conflicts,” Decision Support Systems, vol. 29, no. 1, pp. 1–9, 2000.
View at: Publisher Site | Google Scholar
W. Jiang, B. Wei, C. Xie, and D. Zhou, “An evidential sensor fusion method in fault diagnosis,” Advances in Mechanical Engineering, vol. 8, no. 3, pp. 1–7, 2016.
View at: Publisher Site | Google Scholar
Z.-G. Liu, Q. Pan, J. Dezert, and A. Martin, “Adaptive imputation of missing values for incomplete pattern classification,” Pattern Recognition, vol. 52, pp. 85–95, 2016.
View at: Publisher Site | Google Scholar
W. Jiang, B. Wei, X. Qin, J. Zhan, and Y. Tang, “Sensor data fusion based on a new conflict measure,” Mathematical Problems in Engineering, In press.
View at: Google Scholar
X. Su, S. Mahadevan, W. Han, and Y. Deng, “Combining dependent bodies of evidence,” Applied Intelligence, vol. 44, no. 3, pp. 634–644, 2016.
View at: Publisher Site | Google Scholar
W. Jiang, J. Zhan, D. Zhou, and X. Li, “A method to determine generalized basic probability assignment in the open world,” Mathematical Problems in Engineering, vol. 2016, Article ID 3878634, 11 pages, 2016.
View at: Publisher Site | Google Scholar | MathSciNet
N. R. Sankar and B. S. Prabhu, “Modified approach for prioritization of failures in a system failure mode and effects analysis,” International Journal of Quality and Reliability Management, vol. 18, no. 3, pp. 324–335, 2001.
View at: Publisher Site | Google Scholar
R. K. Sharma, D. Kumar, and P. Kumar, “Systematic failure mode effect analysis (FMEA) using fuzzy linguistic modelling,” International Journal of Quality & Reliability Management, vol. 22, no. 9, pp. 986–1004, 2005.
View at: Publisher Site | Google Scholar
P. Smets and R. Kennes, “The transferable belief model,” Artificial Intelligence, vol. 66, no. 2, pp. 191–234, 1994.
View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet
W.-B. Du, Y. Gao, C. Liu, Z. Zheng, and Z. Wang, “Adequate is better: particle swarm optimization with limited-information,” Applied Mathematics and Computation, vol. 268, pp. 832–838, 2015.
View at: Publisher Site | Google Scholar | MathSciNet
W.-B. Du, X.-L. Zhou, O. Lordan, Z. Wang, C. Zhao, and Y.-B. Zhu, “Analysis of the Chinese Airline Network as multi-layer networks,” Transportation Research Part E: Logistics and Transportation Review, vol. 89, pp. 108–116, 2016.
View at: Publisher Site | Google Scholar
Y. Deng, Y. Liu, and D. Zhou, “An improved genetic algorithm with initial population strategy for symmetric TSP,” Mathematical Problems in Engineering, vol. 2015, Article ID 212794, 6 pages, 2015.
View at: Publisher Site | Google Scholar
X. Ning, J. Yuan, and X. Yue, “Uncertainty-based optimization algorithms in designing fractionated spacecraft,” Scientific Reports, vol. 6, Article ID 22979, 2016.
View at: Publisher Site | Google Scholar
X. Ning, J. Yuan, X. Yue, and A. Ramirez-Serrano, “Induced generalized choquet aggregating operators with linguistic information and their application to multiple attribute decision making based on the intelligent computing,” Journal of Intelligent & Fuzzy Systems, vol. 27, no. 3, pp. 1077–1085, 2014.
View at: Publisher Site | Google Scholar
C. C. Chou, “A generalized similarity measure for fuzzy numbers,” Journal of Intelligent and Fuzzy Systems, vol. 30, no. 2, pp. 1147–1155, 2016.
View at: Publisher Site | Google Scholar
Y. Deng, “Fuzzy analytical hierarchy process based on canonical representation on fuzzy numbers,” Journal of Computational Analysis and Applications, vol. 22, no. 2, pp. 201–228, 2017.
View at: Google Scholar
F. Sabahi and M.-R. Akbarzadeh-T, “A qualified description of extended fuzzy logic,” Information Sciences, vol. 244, pp. 60–74, 2013.
View at: Publisher Site | Google Scholar | MathSciNet
F. Sabahi and M.-R. Akbarzadeh-T, “Introducing validity in fuzzy probability for judicial decision-making,” International Journal of Approximate Reasoning, vol. 55, no. 6, pp. 1383–1403, 2014.
View at: Publisher Site | Google Scholar | MathSciNet
W. Jiang, Y. Luo, X.-Y. Qin, and J. Zhan, “An improved method to rank generalized fuzzy numbers with different left heights and right heights,” Journal of Intelligent and Fuzzy Systems, vol. 28, no. 5, pp. 2343–2355, 2015.
View at: Publisher Site | Google Scholar
C. Fu, J.-B. Yang, and S.-L. Yang, “A group evidential reasoning approach based on expert reliability,” European Journal of Operational Research, vol. 246, no. 3, pp. 886–893, 2015.
View at: Publisher Site | Google Scholar | MathSciNet
Y. Deng, S. Mahadevan, and D. Zhou, “Vulnerability assessment of physical protection systems: a bio-inspired approach,” International Journal of Unconventional Computing, vol. 11, no. 3-4, pp. 227–243, 2015.
View at: Google Scholar
X. Ning, T. Zhang, Y. Wu et al., “Coordinated parameter identification technique for the inertial parameters of non-cooperative target,” PLoS ONE, vol. 11, no. 4, Article ID e0153604, 2016.
View at: Publisher Site | Google Scholar

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Copyright © 2016 Deyun Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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