失效模式及影响分析
环路图
可靠性工程
石川圖
相互依存
故障率
系统动力学
工程类
计算机科学
风险分析(工程)
根本原因
业务
人工智能
政治学
法学
作者
Fatemeh Shaker,Arash Shahin,Saeed Jahanyan
出处
期刊:International Journal of Quality & Reliability Management
[Emerald Publishing Limited]
日期:2021-08-20
卷期号:39 (8): 1977-1995
被引量:9
标识
DOI:10.1108/ijqrm-07-2020-0247
摘要
Purpose This paper aims to develop a system dynamics (SD) model to identify causal relationships among the elements of failure modes and effects analysis (FMEA), i.e. failure modes, effects and causes. Design/methodology/approach A causal loop diagram (CLD) has been developed based on the results obtained from interdependencies and correlations analysis among the FMEA elements through applying the integrated approach of FMEA-quality function deployment (QFD) developed by Shaker et al. (2019). The proposed model was examined in a steel manufacturing company to identify and model the causes and effects relationships among failure modes, effects and causes of a roller-transmission system. Findings Findings indicated interactions among the most significant failure modes, effects and causes. Moreover, corrective actions defined to eliminate or relieve critical failure causes. Consequently, production costs decreased, and the production rate increased due to eliminated/decreased failure modes. Practical implications The application of CLD illustrates causal relationships among FMEA elements in a more effective way and results in a more precise recognition of the root causes of the potential failure modes and their easy elimination/decrease. Therefore, applying the proposed approach leads to a better analysis of the interactions among FMEA elements, decreased system's failure rate and increased system availability. Originality/value The literature review indicated a few studies on the application of SD methodology in the maintenance area, and no study was performed on the causal interactions among FMEA elements through an FMEA-QFD based SD approach. Although the interactions of these elements are significant and helpful in risks ranking, researchers fail to investigate them sufficiently.
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