使用基于深度学习网络的指数加权移动平均控制图监测马型I审查数据
1Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan. g9321801@yuntech.edu.tw.
Scientific reports
|March 19, 2024
概括
这项研究通过将深度学习与EWMA控制图表集成到类型I权利审查数据中来增强可靠性测试. 新方法提高了对马分布的监测效率,优于传统方法.
科学领域:
- 统计过程控制 统计过程控制
- 在可靠性工程中的机器学习.
- 对被审查数据的数据分析.
背景情况:
- 传统的控制图表与可靠性测试中常见的类型I右边审查数据作斗争.
- 使用条件预期值 (CEV) 或条件中位数 (CM) 的现有方法提供了改进,但也有局限性.
- 深度学习显示了增强完整数据监控的前景.
研究的目的:
- 开发和评估一种新的控制图表,将深度学习与EWMA-CEV和EWMA-CM结合起来,用于玛型I右边审查的数据.
- 在不同的条件下,与传统的EWMA图表相比,评估拟议方法的效率.
- 通过模拟和现实世界的案例研究来证明新方法的实际应用性.
主要方法:
- 集成深度学习算法与指数加权移动平均 (EWMA) 控制图.
- 开发利用受审查数据的条件预期值 (CEV) 和条件中位数 (CM) 的图表.
- 统计模拟和现实世界的案例研究来验证性能.
主要成果:
- 拟议的深度学习增强的EWMA-CEV和EWMA-CM图表显示了对玛型I右控数据的卓越效率.
- 该方法在各种斜率系数值和审查率上显示了改进的性能.
- 与CEV和CM的传统EWMA图表相比,持续观察到的表现优于传统EWMA图表.
结论:
- 深度学习的整合大大提高了对被审查的可靠性数据的控制图的效率.
- 这种新的方法提供了一个强大的解决方案,用于监测马分布的I型右边审查.
- 这项研究为质量控制和可靠性工程实践提供了宝贵的进步.
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