An integrated risk assessment method using Z‐fuzzy clouds and generalized TODIM

Abstract

As a systematic risk assessment instrument, failure mode, and effects analysis (FMEA) has been extensively applied for enhancing systems’ quality. However, the classical FMEA technique has limitations in many application scenarios. Although fuzzy logic‐based risk assessment techniques are introduced to enhance conventional FMEA, they still have limitations, for example, needing prior presumptions, neglecting decision‐makers’ bounded rationality, and lacking the capability to reflect the judgmental reliability and randomness of experts’ evaluations simultaneously. To cover these limitations, an extended FMEA method is presented in this paper by using Z‐fuzzy clouds and generalized TODIM (An acronym in Portuguese of interactive and multiple attribute decision‐making). First, a novel concept of Z‐fuzzy clouds is proposed to handle various factors of uncertainties by combining the advantages of the Z‐number theory, the interval‐valued intuitionistic fuzzy sets theory, and the cloud model theory, and the basic operations and Minkowski‐type distance measure of Z‐fuzzy clouds are also defined and discussed. Furthermore, more risk factors are added into the conventional three risk factors and to further build a multiple dimension risk factor system for better evaluating failures. Additionally, the experts’ bounded rationality and the risk factors’ interrelationships are considered by two developed weighting models. Finally, a real study case, sensitivity analysis, and several comparisons are performed to validate the effectiveness of the proposed model. Results demonstrate that the proposed model is more rational compared with the previous several methods.

A failure tracing method with hierarchical digraph and meta action for complex electromechanical systems

“…Based on the constructed digraph model D 3 of complex EMS, the crucial failed meta actions can be obtained. To trace failure root causes and determine the crucial failure modes, the FMEA method, a powerful tool to analyze failure modes and trace root causes, [48][49][50] is applied to analyze the failure modes of crucial meta actions and identify the risk rankings of all failure modes.…”

Failure mode and effect analysis using the hesitant intuitionistic fuzzy hybrid GRP approach with ordered comprehensive weights

“…Other methods, like cloud model, 11 Z‐numbers, 25 and rough sets, 26 have also been applied to improve FMEA evaluation conversion in recent years. Besides, combinations of multiple methods have been studied 27,28 . Among all the improvements, the most common is fuzzy set theory and its related methods 29 .…”

“…Besides, combinations of multiple methods have been studied. 27,28 Among all the improvements, the most common is fuzzy set theory and its related methods. 29 Since Zadeh 5 put forward the fuzzy set in 1965, which describes the fuzziness of the element by the membership degree, many scholars 1,30 have applied it to FMEA.…”

Solving a System of Linear Equations Based on Z-Numbers to Determinate the Market Balance Value

“…In addition, the contract (x, A, B) can also be introduced as a Z-value. Semantically, using the value proposition developed in [5], the variable x can be interpreted in such a way that the probability that x takes a value of A is equal to B. It means as follows:…”

“…It should be mentioned that these numbers are directly related to quantity; it can be observed that they have more ability to describe the uncertainty environment than fuzzy numbers that do not have this feature. In this regard, several researchers have conducted many studies in various felds by utilizing Z-numbers [4][5][6][7][8][9][10][11][12].…”