An Advanced FMEA Method Integrating Consensus‐Based Bayesian Network Model and Risk Propagation Model.
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| Title: | An Advanced FMEA Method Integrating Consensus‐Based Bayesian Network Model and Risk Propagation Model. |
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| Authors: | Teng, Fei1 (AUTHOR), Hong, Yifang1 (AUTHOR), Wu, Yifan1 (AUTHOR), Liu, Peide1,2 (AUTHOR) peide.liu@gmail.com, Cuccureddu, Floriano (AUTHOR) fcuccuredd@wiley.com |
| Source: | International Journal of Intelligent Systems. 7/20/2026, Vol. 2026, p1-28. 28p. |
| Subjects: | Failure mode & effects analysis, Bayesian analysis, Risk assessment, Multiple criteria decision making, Expert systems, Mathematical optimization, Supply chains |
| Abstract: | Traditional failure mode and effects analysis (FMEA) often overlooks interdependencies among failure modes (FMs), leading to incomplete risk assessments. This paper presents an enhanced FMEA framework that integrates Bayesian networks (BNs) and risk propagation operators to address FM interactions, introducing propagation (P) as a novel risk factor (RF) alongside the conventional occurrence (O), detection (D), and severity (S). The proposed framework is structured into three hierarchical layers based on distinct risk assessment mechanisms. Layer 1 (expert direct assessment) assesses D and S through a two‐round evaluation process, including an expert weight determination model that combines competency scores with a dual‐dimensional reliability assessment. Layer 2 (model‐dependent inference) first builds a BN topology and uses a two‐stage consensus model to obtain acceptable conditional probability tables, from which O is derived; then, based on the constructed BN, it quantifies P through propagation operators and BN inference, capturing both forward and backward risk propagation. Layer 3 (multicriteria decision‐making) integrates the four RFs using a dynamic grey relational analysis method for risk prioritization. In this layer, an optimization model is formulated to maximize global benefits and minimize individual losses, thereby determining the relative importance of each RF. Finally, a case study on risk management in the new energy vehicle supply chain is conducted, and the effectiveness and applicability of the proposed framework are validated through stress tests, sensitivity analyses, and comparative analyses. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Intelligent Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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