基于MOGA和扩展RPN的方法用于制造业的启发式关键可靠性特征分析
Jishan Zhang1, Yihai He1, Xin Zheng1
1School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China.
ISA transactions
|May 12, 2024
概括
本研究引入了一种新的模型,用于识别制造业中使用大质量数据的关键可靠性特征 (KRC). 这种方法通过专注于生产过程中的关键因素来提高产品的可靠性.
科学领域:
- 制造业 工程 制造工程
- 质量管理质量管理.
- 可靠性工程可靠性工程
背景情况:
- 产品的可靠性至关重要,必须在制造过程中整合起来,不断发展的质量概念朝着"适用性"发展.
- 在制造过程中确定影响可靠性的关键因素对于有效的质量控制至关重要.
研究的目的:
- 为制造业提出一个启发式的关键可靠性特征 (KRC) 分析模型,集成大质量数据.
- 根据已识别的KRC,开发一个数据驱动的框架,用于在制造业中进行主动的可靠性控制.
主要方法:
- 使用可靠性 - 操作过程 质量 - 输出产品可靠性 (RQR) 链和大质量数据开发了数据驱动的KRC分析模型.
- 使用数据挖掘和多目标遗传算法 (MOGA) 建立了一个关键可靠性特征识别方法.
- 建议使用扩展的风险优先级号码 (RPN) 来对KRC进行排名,考虑制造数据和产品故障风险.
主要成果:
- 拟议的模型有效地识别了在制造过程中对产品可靠性至关重要的一些关键特征.
- 建立了一个主动的可靠性控制框架,由已识别的KRC驱动.
- 该方法使用地铁机车绝缘基的例子进行了验证.
结论:
- 整合大质量数据和先进的分析方法可以准确识别制造业的关键可靠性特征.
- 建议的启发式KRC分析模型和扩展的RPN提供了一个强大的框架,通过主动制造质量控制来提高产品可靠性.
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