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Failure mode and effects analysis (FMEA) is an engineering and management technique, which is widely used to define, identify, and eliminate known or potential failures, problems, errors, and risk from the design, process, service, and so on. In a typical FMEA, the risk evaluation is determined by using the risk priority number (RPN), which is obtained by multiplying the scores of the occurrence, severity, and detection. However, because of the uncertainty in FMEA, the traditional RPN has been criticized because of several shortcomings. In this paper, an evidential downscaling method for risk evaluation in FMEA is proposed. In FMEA model, we utilize evidential reasoning approach to express the assessment from different experts. Multi-expert assessments are transformed to a crisp value with weighted average method. Then, Euclidean distance from multi-scale is applied to construct the basic belief assignments in Dempster-Shafer evidence theory application. According to the proposed method, the number of ratings is decreased from 10 to 3, and the frame of discernment is decreased from 210 to 23, which greatly decreases the computational complexity. Dempster's combination rule is utilized to aggregate the assessment of risk factors. We illustrate a numerical example and use the proposed method to deal with the risk priority evaluation in FMEA. The results and comparison show that the proposed method is more flexible and reasonable for real applications. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Dependence assessment among human errors in human reliability analysis (HRA) is an important issue. Many of the dependence assessment methods in HRA rely heavily on the expert's opinion, thus are subjective and may sometimes cause inconsistency. In this article, we propose a computational model based on the Dempster-Shafer evidence theory (DSET) and the analytic hierarchy process (AHP) method to handle dependence in HRA. First, dependence influencing factors among human tasks are identified and the weights of the factors are determined by experts using the AHP method. Second, judgment on each factor is given by the analyst referring to anchors and linguistic labels. Third, the judgments are represented as basic belief assignments (BBAs) and are integrated into a fused BBA by weighted average combination in DSET. Finally, the CHEP is calculated based on the fused BBA. The proposed model can deal with ambiguity and the degree of confidence in the judgments, and is able to reduce the subjectivity and improve the consistency in the evaluation process. [ABSTRACT FROM AUTHOR]

Abstract: Decision-making is an essential but difficult problem under uncertain environment. Many researchers have addressed it in the framework of the AHP. The AHP has many advantages. However, the incomplete information resulted from the limited precision of human assessments reduces its effectiveness in many applications. In this paper, a new method is proposed for decision-making by incomplete information based on evidence distance with using of DS theory to extend the AHP. The main feature of the proposed method is the weight of criteria are assigned with the consistency and similarity of the opinions from experts or decision makers. The operation of preferences is defined in the framework of DS theory. The final decision is obtained with pignistic probability transformation. Numerical examples are used to illustrate the reasonability of the proposed method. [Copyright &y& Elsevier]

Highlights: [•] We propose a method to design the physical protection system (PPS). [•] Evidence theory is used to evaluate the PPS and optimally locate the components. [•] Components’ allocation significantly affects the effectiveness of PPS. [ABSTRACT FROM AUTHOR]

Abstract: The Dempster–Shafer evidence theory (D–S theory) is one of the primary tools for knowledge representation and uncertain reasoning, and has been widely used in many information fusion systems. However, how to determine the basic probability assignment (BPA), which is the main and first step in D–S theory, is still an open issue. In this paper, based on the normal distribution, a method to obtain BPA is proposed. The training data are used to build a normal distribution-based model for each attribute of the data. Then, a nested structure BPA function can be constructed, using the relationship between the test data and the normal distribution model. A normality test and normality transformation are integrated into the proposed method to handle non-normal data. The missing attribute values in datasets are addressed as ignorance in the framework of the evidence theory. Several benchmark pattern classification problems are used to demonstrate the proposed method and to compare against existing methods. Experiments provide encouraging results in terms of classification accuracy, and the proposed method is seen to perform well without a large amount of training data. [Copyright &y& Elsevier]