scholarly journals An Intuitionistic Evidential Method for Weight Determination in FMEA Based on Belief Entropy

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 211 ◽  
Author(s):  
Zeyi Liu ◽  
Fuyuan Xiao
Keyword(s):  
Decision Making ◽  
Failure Modes ◽  
Evidence Theory ◽  
Team Members ◽  
Domain Experts ◽  
Open Issue ◽  
Belief Entropy ◽  
Potential Failure ◽  
The Impact ◽  
Effective Analysis

Failure Mode and Effects Analysis (FMEA) has been regarded as an effective analysis approach to identify and rank the potential failure modes in many applications. However, how to determine the weights of team members appropriately, with the impact factor of domain experts’ uncertainty in decision-making of FMEA, is still an open issue. In this paper, a new method to determine the weights of team members, which combines evidence theory, intuitionistic fuzzy sets (IFSs) and belief entropy, is proposed to analyze the failure modes. One of the advantages of the presented model is that the uncertainty of experts in the decision-making process is taken into consideration. The proposed method is data driven with objective and reasonable properties, which considers the risk of weights more completely. A numerical example is shown to illustrate the feasibility and availability of the proposed method.

eTTCampus

2013 ◽  
pp. 528-540
Author(s):  
David E. Gray ◽  
Malcolm Ryan
Keyword(s):  
Decision Making ◽  
Project Management ◽  
Critical Analysis ◽  
Virtual Campus ◽  
Team Members ◽  
Management Processes ◽  
Funded Project ◽  
Effective Use ◽  
The Impact ◽  
Training Opportunities

This chapter critically examines innovative approaches to the evaluation of a European funded project involving nine countries in the development of a virtual campus to provide training opportunities in ICT for teachers and trainers across Europe. It explores project management processes and decision-making and the impact on outcomes as well as relationships between project team members. It concludes with recommendations for the more effective use of a range of these approaches, asserting that a critical analysis of the processes of engagement is as important as the outcomes.

scholarly journals A Novel Belief Entropy for Measuring Uncertainty in Dempster-Shafer Evidence Theory Framework Based on Plausibility Transformation and Weighted Hartley Entropy

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 163 ◽  
Author(s):  
Qian Pan ◽  
Deyun Zhou ◽  
Yongchuan Tang ◽  
Xiaoyang Li ◽  
Jichuan Huang
Keyword(s):  
Evidence Theory ◽  
Mass Function ◽  
Probability Mass Function ◽  
Main Work ◽  
Basic Probability ◽  
Open Issue ◽  
Belief Entropy ◽  
Probability Mass ◽  
Hartley Entropy ◽  
Theory Framework

Dempster-Shafer evidence theory (DST) has shown its great advantages to tackle uncertainty in a wide variety of applications. However, how to quantify the information-based uncertainty of basic probability assignment (BPA) with belief entropy in DST framework is still an open issue. The main work of this study is to define a new belief entropy for measuring uncertainty of BPA. The proposed belief entropy has two components. The first component is based on the summation of the probability mass function (PMF) of single events contained in each BPA, which are obtained using plausibility transformation. The second component is the same as the weighted Hartley entropy. The two components could effectively measure the discord uncertainty and non-specificity uncertainty found in DST framework, respectively. The proposed belief entropy is proved to satisfy the majority of the desired properties for an uncertainty measure in DST framework. In addition, when BPA is probability distribution, the proposed method could degrade to Shannon entropy. The feasibility and superiority of the new belief entropy is verified according to the results of numerical experiments.

scholarly journals An Improved Total Uncertainty Measure in the Evidence Theory and Its Application in Decision Making

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 487 ◽  
Author(s):  
Miao Qin ◽  
Yongchuan Tang ◽  
Junhao Wen
Keyword(s):  
Decision Making ◽  
Evidence Theory ◽  
Uncertain Information ◽  
Uncertainty Measure ◽  
Total Uncertainty ◽  
Practical Applications ◽  
Basic Probability ◽  
Belief Entropy ◽  
Element Number ◽  
Theory Framework

Dempster–Shafer evidence theory (DS theory) has some superiorities in uncertain information processing for a large variety of applications. However, the problem of how to quantify the uncertainty of basic probability assignment (BPA) in DS theory framework remain unresolved. The goal of this paper is to define a new belief entropy for measuring uncertainty of BPA with desirable properties. The new entropy can be helpful for uncertainty management in practical applications such as decision making. The proposed uncertainty measure has two components. The first component is an improved version of Dubois–Prade entropy, which aims to capture the non-specificity portion of uncertainty with a consideration of the element number in frame of discernment (FOD). The second component is adopted from Nguyen entropy, which captures conflict in BPA. We prove that the proposed entropy satisfies some desired properties proposed in the literature. In addition, the proposed entropy can be reduced to Shannon entropy if the BPA is a probability distribution. Numerical examples are presented to show the efficiency and superiority of the proposed measure as well as an application in decision making.

scholarly journals A Case Study on Improving Intensive Care Unit (ICU) Services Reliability: By Using Process Failure Mode and Effects Analysis (PFMEA)

2016 ◽  
Vol 8 (9) ◽  
pp. 207 ◽  
Author(s):  
Taraneh Yousefinezhadi ◽  
Farnaz Attar Jannesar Nobari ◽  
Faranak Behzadi Goodari ◽  
Mohammad Arab
Keyword(s):  
Intensive Care ◽  
Human Error ◽  
Failure Modes ◽  
Classification Model ◽  
Group Discussions ◽  
Team Members ◽  
Acceptable Risks ◽  
Process Failure ◽  
Potential Failure ◽  
Fmea Method

<p><strong>INTRODUCTION:</strong> In any complex human system, human error is inevitable and shows that can’t be eliminated by blaming wrong doers. So with the aim of improving Intensive Care Units (ICU) reliability in hospitals, this research tries to identify and analyze ICU’s process failure modes at the point of systematic approach to errors.</p><p><strong>METHODS:</strong> In this descriptive research, data was gathered qualitatively by observations, document reviews, and Focus Group Discussions (FGDs) with the process owners in two selected ICUs in Tehran in 2014. But, data analysis was quantitative, based on failures’ Risk Priority Number (RPN) at the base of Failure Modes and Effects Analysis (FMEA) method used.<strong> </strong>Besides, some causes of failures were analyzed by qualitative Eindhoven Classification Model (ECM).</p><p><strong>RESULTS:</strong> Through<strong> </strong>FMEA methodology, 378 potential failure modes from 180 ICU activities in hospital A and 184 potential failures from 99 ICU activities in hospital B were identified and evaluated. Then with 90% reliability (RPN≥100), totally 18 failures in hospital A and 42<strong> </strong>ones in hospital B were identified as non-acceptable risks and then their causes were analyzed by ECM.</p><p><strong>CONCLUSIONS</strong>: Applying of modified PFMEA for improving two selected ICUs’ processes reliability in two different kinds of hospitals shows that this method empowers staff to identify, evaluate, prioritize and analyze all potential failure modes and also make them eager to identify their causes, recommend corrective actions and even participate in improving process without feeling blamed by top management. Moreover, by combining FMEA and ECM, team members can easily identify failure causes at the point of health care perspectives.</p>

Computational Methods to Predict and Avoid Design Failure

2005 ◽  
Author(s):  
Brian A. Mitchell ◽  
Daniel A. McAdams ◽  
Robert B. Stone ◽  
Irem Y. Tumer
Keyword(s):  
Failure Modes ◽  
Design Method ◽  
Component Failure ◽  
Initial Validation ◽  
Component Selection ◽  
Previous Design ◽  
Potential Failure ◽  
Design Projects ◽  
The Impact ◽  
Initial Results

Component selection can be a difficult task for designers, and the components they choose can have a large impact on the robustness of the design. Using previous methods to predict and identify potential failure modes, known as the function-failure design method (FFDM), the impact on failure of selecting a particular component over another can be explored based on failure results from previous design endeavors using the same component. This assists designers in selecting the component that is best suited for the application. Since the predicted distribution of failure modes changes depending on the selected component, failure reduction is possible through component selection. Through this method of component selection, risk can be decreases and potential failures can be eliminated. Experiments based on undergraduate student competition design projects are presented to illustrate this method’s ability to predict failure modes. Initial results indicate that the predictions are accurate and meaningful to designers. The experiment also serves as initial validation previous work in the area of failure prediction.

scholarly journals A New Belief Entropy in Dempster–Shafer Theory Based on Basic Probability Assignment and the Frame of Discernment

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 691 ◽  
Author(s):  
Jiapeng Li ◽  
Qian Pan
Keyword(s):  
Shannon Entropy ◽  
Information Uncertainty ◽  
Numerical Examples ◽  
New Model ◽  
Dempster Shafer Theory ◽  
Basic Probability ◽  
Open Issue ◽  
Belief Entropy ◽  
Shafer Theory ◽  
The Impact

Dempster–Shafer theory has been widely used in many applications, especially in the measurement of information uncertainty. However, under the D-S theory, how to use the belief entropy to measure the uncertainty is still an open issue. In this paper, we list some significant properties. The main contribution of this paper is to propose a new entropy, for which some properties are discussed. Our new model has two components. The first is Nguyen entropy. The second component is the product of the cardinality of the frame of discernment (FOD) and Dubois entropy. In addition, under certain conditions, the new belief entropy can be transformed into Shannon entropy. Compared with the others, the new entropy considers the impact of FOD. Through some numerical examples and simulation, the proposed belief entropy is proven to be able to measure uncertainty accurately.

scholarly journals An Improved Assessment Method for FMEA for a Shipboard Integrated Electric Propulsion System Using Fuzzy Logic and DEMATEL Theory

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3162 ◽  
Author(s):  
Liu ◽  
Guo ◽  
Zhang
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Electric Propulsion ◽  
Failure Modes ◽  
Search Algorithm ◽  
Assessment Method ◽  
Security Threats ◽  
System Failures ◽  
Domain Experts ◽  
Risk Levels

Shipboard integrated electric propulsion systems (IEPSs) are prone to suffer from system failures and security threats because of their complex functional structures and poor operational environments. An improved assessment method for failure mode and effects analysis (FMEA), integrating fuzzy logic and decision–making trial and evaluation laboratory (DEMATEL) theory, is proposed to enhance the system’s reliability and handle the correlation effects between failure modes and causes. In this method, information entropy and qualitative analysis are synthesized to determine the credibility weights of domain experts. Each risk factor and its relative importance are evaluated by linguistic terms and fuzzy ratings. The benchmark adjustment search algorithm is designed to obtain the alpha-level sets of fuzzy risk priority numbers (RPNs) for defuzzification. The defuzzified RPNs are regarded as the inputs for the DEMATEL technique to investigate the causal degrees of failure modes and causes. Accordingly, the risk levels of the failure modes are prioritized with respect to the causal degrees. The practical application to the typical failure modes of the propulsion subsystem is provided. The assessment results show that this system contributes to risk priority decision-making and disastrous accident prevention.

scholarly journals Multiscale Probability Transformation of Basic Probability Assignment

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Meizhu Li ◽  
Xi Lu ◽  
Qi Zhang ◽  
Yong Deng
Keyword(s):  
Decision Making ◽  
Probability Function ◽  
Evidence Theory ◽  
Belief Function ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Probability Transformation ◽  
Basic Probability ◽  
Open Issue ◽  
Belief Model

Decision making is still an open issue in the application of Dempster-Shafer evidence theory. A lot of works have been presented for it. In the transferable belief model (TBM), pignistic probabilities based on the basic probability assignments are used for decision making. In this paper, multiscale probability transformation of basic probability assignment based on the belief function and the plausibility function is proposed, which is a generalization of the pignistic probability transformation. In the multiscale probability function, a factorqbased on the Tsallis entropy is used to make the multiscale probabilities diversified. An example showing that the multiscale probability transformation is more reasonable in the decision making is given.

eTTCampus

2013 ◽  
pp. 383-401
Author(s):  
David E. Gray ◽  
Malcolm Ryan
Keyword(s):  
Decision Making ◽  
Project Management ◽  
Critical Analysis ◽  
Virtual Campus ◽  
Team Members ◽  
Management Processes ◽  
Funded Project ◽  
Effective Use ◽  
The Impact ◽  
Training Opportunities

This chapter critically examines innovative approaches to the evaluation of a European funded project involving nine countries in the development of a virtual campus to provide training opportunities in ICT for teachers and trainers across Europe. It explores project management processes and decision-making and the impact on outcomes as well as relationships between project team members. It concludes with recommendations for the more effective use of a range of these approaches, asserting that a critical analysis of the processes of engagement is as important as the outcomes.

An integrated FMEA approach using Best-Worst and MARCOS methods based on D numbers for prioritization of failures

2021 ◽  
pp. 1-14
Author(s):  
Jianping Fan ◽  
Shuting Wang ◽  
Meiqin Wu
Keyword(s):  
Failure Modes ◽  
Team Members ◽  
Analysis Technique ◽  
Prioritization Method ◽  
Wide Range ◽  
Potential Failure ◽  
Fmea Method ◽  
Priority Model ◽  
D Numbers ◽  
Evaluation Information

Failure modes and effects analysis (FMEA) is a useful reliability analysis technique to identify potential failure modes in a wide range of industries. However, the conventional FMEA method is deficient in dealing with the risk evaluation and prioritization method. To overcome the shortcomings, this paper presents a new risk priority model using Best-Worst Method based on D numbers (D-BWM) and the Measurement of Alternatives and Ranking according to COmpromise Solution based on D numbers (D-MARCOS). First, D numbers are used to deal with the uncertainty of FMEA team members’ subjective judgment. Second, the distance-based method is proposed to determine the objective weight of each team member. Then, the D-BWM was used to determine the weight of risk factors. The combination rule of D number theory combined the evaluation information of multiple members into group opinions. Finally, D-MARCOS method is proposed to obtain the risk priority of the failure modes. An example and the results of comparative analysis show the method is effective.

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