New Failure Mode and Effects Analysis based on D Numbers Downscaling Method

被引:1
|
作者
Liu, B. [1 ]
Hu, Y. [2 ]
Deng, Y. [2 ,3 ]
机构
[1] Jinan Univ, Coll Infomat Sci & Technol, Guangzhou, Guangdong, Peoples R China
[2] Jinan Univ, Big Data Decis Inst, Guangzhou 510632, Guangdong, Peoples R China
[3] Univ Elect Sci & Technol China, Inst Fundamental & Frontier Sci, Chengdu 610054, Peoples R China
基金
中国国家自然科学基金;
关键词
Failure mode and effects analysis; Dempster-Shafer evidence theory; basic belief assignment; belief function; risk priority number; D numbers; evidential downscaling; aggregate assessment; DEPENDENCE ASSESSMENT; MATRIX; SETS;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Failure mode and effects analysis (FMEA) is extensively applied to process potential faults in systems, designs, and products. Nevertheless, traditional FMEA, classical risk priority number (RPN), acquired by multiplying the ratings of occurrence, detection, and severity, risk assessment, is not effective to process the uncertainty in FMEA. Many methods have been proposed to solve the issue but deficiencies exist, such as huge computing quality and the mutual exclusivity of propositions. In fact, because of the subjectivity of experts, the boundary of two adjacent evaluation ratings is fuzzy so that the propositions are not mutually exclusive. To address the issues, in this paper, a new method to evaluate risk in FMEA based on D numbers and evidential downscaling method, named as D numbers downscaling method, is proposed. In the proposed method, D numbers based on the data are constructed to process uncertain information and aggregate the assessments of risk factors, for they permit propositions to be not exclusive mutually. Evidential downscaling method decreases the number of ratings from 10 to 3, and the frame of discernment from 2(10) to 2(3), which greatly reduce the computational complexity. Besides, a numerical example is illustrated to validate the high efficiency and feasibility of the proposed method.
引用
收藏
页码:205 / 220
页数:16
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