Failure Mode And Effect Analysis: An Interval-Valued Intuitionistic Fuzzy Cloud Theory-Based Method


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Failure mode and effect analysis (FMEA) is a proactive quality management instrument to improve the reliability of systems. Nevertheless, the classical FMEA technique has suffered from many weaknesses, e.g., inability to handle inaccurate information, strong sensitiveness to variations in assessments. Although fuzzy theories are utilized to enhance the classical FMEA, they still have some deficiencies, e.g., requiring extra assumptions, lacking mechanism to describe the hesitation and randomness of assessment information simultaneously, ignoring the psychological effects of experts, and considering only three risk aspects among most of them. Hence, this work presents a novel concept of interval-valued intuitionistic fuzzy clouds (IVIFCs), which combines the merit of interval-valued intuitionistic fuzzy set in reflecting vagueness and hesitation of decision information and the strength of cloud model in manipulating randomness of quantitative information, and a new FMEA based on IVIFCs. Then, the individual bounded rationalities are determined by a developed weighting method considering both subjective and objective importance. Moreover, a hierarchical structure containing eight risk elements is established to identify risk orders of failures. Additionally, a well-defined Excel computational program is presented to reduce the calculation burden effectively. Finally, a real application of a machine tool is conducted by the proposed FMEA to illustrate its effectiveness and superiority. (C) 2020 Elsevier B.V. All rights reserved.
Failure mode and effect analysis, Cloud model theory, Interval-valued intuitionistic fuzzy set theory, Synthetic weighting method, Hierarchical TOPSIS
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