Dynamic uncertain causality graph based on Intuitionistic fuzzy sets and its application to root cause analysis

Li, Li; Yue, Weichao

doi:10.1007/s10489-019-01520-6

Cited by 23 publications

(7 citation statements)

References 31 publications

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“…In 1965, Zadeh defined the mathematical meaning of IFSs for the first time [21] and opened a new chapter of fuzzy mathematics. In 1986, Atanassov popularized fuzzy sets, and it was also the first time to express the relationship between an element and a specific set with the three indexes of membership degree, nonmembership degree, and hesitation, and he put forward the concept of IFSs [22]. Let the set X be a universe of discourse, the fuzzy set F on X is represented by membership degree μ F , μ F :X ⟶ [0, 1], and then the membership degree of x in the set F is denoted by μ F (x).…”

Section: Evaluation Of Ifns On Fms and Influencing Factorsmentioning

confidence: 99%

“…According to formula (21), the consensus degree of each evaluator is C 1 � 0.5722, C 2 � 0.7422, C 3 � 0.8537. If the critical value of consensus degree C is set to 0.5, then all three evaluators meet the critical value, and it is generally considered that consensus can be reached only when the critical value C � 0.85. en, it is modified according to formula (22), where the iterative operation is taken as ξ � 0.5. After three iterations, the consensus reached is…”

Section: An Example In Manufacturing Systemsmentioning

confidence: 99%

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A Potential Failure Mode and Effect Analysis Method of Electromagnet Based on Intuitionistic Fuzzy Number in Manufacturing Systems

Pang

Dai

2021

Mathematical Problems in Engineering

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Failure mode and effect analysis (FMEA) is a systematic activity in the stage of product design and process design. However, the traditional FMEA has some shortcomings in practical application, such as too many evaluation languages, uncertain weights of influencing factors, and uncertain weights of evaluation members. This paper presents an FMEA evaluation method in manufacturing system based on similarity measure, nonlinear programming model, and intuitionistic fuzzy number (IFN). Firstly, the IFN is used to evaluate failure mode, which overcomes the defect of traditional FMEA evaluation value. Secondly, the weight of failure evaluation team members is solved according to the concept of similarity measure to make up for the blank of evaluation members’ weight aiming at the shortage of unknown weight. Then, the definition of consensus measure is introduced to make the evaluators reach a consensus, and the weights of influencing factors of failure modes (FMs) are calculated. Finally, the weights of evaluators and influencing factors are calculated by IFN algorithm and score function, and the score value of each FM is obtained to rank instead of risk priority number (RPN). The objectivity and practicability of the new method are verified by the example of failure mode for an attractive electromagnet manufacturing system.

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Section: Evaluation Of Ifns On Fms and Influencing Factorsmentioning

confidence: 99%

Section: An Example In Manufacturing Systemsmentioning

confidence: 99%

A Potential Failure Mode and Effect Analysis Method of Electromagnet Based on Intuitionistic Fuzzy Number in Manufacturing Systems

Pang

Dai

2021

Mathematical Problems in Engineering

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“…流程工业智能制造中的知识表示定义了流程工业中知识的表现形式, 知识表示方法依据后期知识处理中所采用的方法不同而不同. 当前知识表示常用的方法主要有关联规则表示法 [24,25] 、模糊认知图表示法 [26,27] 、动态不确定因果图 [28,29] 、Petri 网表示法 [30] 、语义网络表示 [31] 、面向对象表示法 [32] 、框架表示法 [33] 等. 知识推理作为流程工业知识处理的重要环节, 一直备受关注.…”

Section: 知识处理unclassified

Knowledge-driven process industry smart manufacturing

Gui¹,

Zeng²,

Chen³

et al. 2020

Sci. Sin.-Inf.

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“…The fault of Elevator can be diagnosed by the performance characteristics embodied in the working process of elevator. That is to say, by monitoring the operation of Elevator, the characteristic data are measured and sent to the neural network of fault features, filtering out interference signals, and then evaluating and processing them through the fuzzy neural network [13,14]. Single or multiple faults may occur at a certain time.…”

Section: Elevator Fault Characteristic Analysis 31 Elevator Fault Chmentioning

confidence: 99%

Research on Fault Diagnosis Based on Dynamic causality diagram and Fuzzy Reasoning Fusion Method

2020

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With the progress of urbanization, the demand for elevators has upgraded from safe operation to comfortable, efficient, and all-round demand. The abnormal operation of the elevator is difficult to diagnose due to the complexity of the fault. This paper proposes a fault diagnosis method based on dynamic causality diagram and fuzzy reasoning. The dynamic causality diagram is extended, the intermediate module nodes are added, the description of the intermediate process of the elevator control system is solved, and the complete expression of knowledge is realized. The control timing of the elevator operation is introduced into the network structure of the dynamic causality diagram, which enhances the dynamic characteristics of the network. The causal cycle logic of the dynamic causality diagram is used to represent input and output signals and faults in elevator control systems. In the update of fuzzy rules, the real-time of fuzzy reasoning is enhanced, the search space of fuzzy rule matching is reduced, and the efficiency is improved. This paper combines actual field measurements and experimental data for fault diagnosis. Finally, the simulation, diagnosis and maintenance decision of the fault are realized, and an intelligent solution for elevator fault diagnosis is further proposed.

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Dynamic uncertain causality graph based on Intuitionistic fuzzy sets and its application to root cause analysis

Cited by 23 publications

References 31 publications

A Potential Failure Mode and Effect Analysis Method of Electromagnet Based on Intuitionistic Fuzzy Number in Manufacturing Systems

A Potential Failure Mode and Effect Analysis Method of Electromagnet Based on Intuitionistic Fuzzy Number in Manufacturing Systems

Knowledge-driven process industry smart manufacturing

Research on Fault Diagnosis Based on Dynamic causality diagram and Fuzzy Reasoning Fusion Method

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