失效模式的逆向评价及影响分析模型:一种新的可靠性能度量方法

Khaled M Alsubiaee, Abdulmohsan F Alotaibi, Abdulaziz Alshehri, Mohammed Alassaf
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引用次数: 2

摘要

第二次世界大战之后,工业时代开始了。因此,风险管理哲学的关注点从错误解决转向通过预测原因来预防错误。失效模式和影响分析(FMEA)是减少失效最常用的方法之一。FMEA根据风险优先级数(RPN)逼近概率。众所周知,RPN评分系统在测量严重程度(S)、发生(O)和故障检测(D)的过程中存在局限性。RPN在确定故障的相对重要性方面并不准确。利用Kruskal-Wallis检验,R-FMEA提供了一种主动的、更可靠的方法。本文通过实例讨论了一种新的统计模型(R-FMEA)如何提高FMEA预测误差的可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reverse Evaluation of Failure Mode and Effects Analysis Model: A New Reliable Performance Measurement
After the World War II (WWII), the industrial era began. Therefore the risk management philosophy concerns shifted from error resolving to error prevention by predicting the causes. One of the most common processes used to decrease the failures is utilizing the Failure Mode and Effects Analysis (FMEA). FMEA approximates the probability based on Risk Priority Number (RPN). RPN is known for limitations in the process of RPN scoring system measuring the severity (S), occurrence (O), and detection of the failure (D). RPN is not an accurate, in determining the relative importance of failure. R-FMEA provides a proactive more reliable method using Kruskal-Wallis test. This article discusses with examples how a new suggested statistical model (R-FMEA) is enhancing the reliability of FMEA to predict errors.
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