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一项关于制造业知识转移的调查数据分析
Seung Hwan Bang1,2, Ronay Ak1, Anantha Narayanan3
1Systems Integration Division, Engineering Laboratory, National Institute of Standards and Technology (NIST), Gaithersburg, MD, 20899, USA.
概括
制造业数据分析模型随着时间的推移因非静止环境而退化. 知识转移 (KT) 方法可以使模型适应新的条件,保持准确性并减少再培训需求.
科学领域:
- 制造业数据分析数据分析
- 机器学习操作 (MLOps)
背景情况:
- 数据分析模型假设静态环境,这在制造业中往往是错误的.
- 在非静止制造环境中,模型准确性随着时间的推移而降低,需要定期重新培训.
研究的目的:
- 调查适用于制造数据分析的知识转移 (KT) 方法.
- 调查 KT 适用于将模型适应非静止制造环境的适应性.
- 为在制造业中选择适当的KT方法提供一个机制.
主要方法:
- 在各种应用中调查现有的知识转移方法.
- 分析基于非静态环境类型,数据可用性和知识来源的知识技术方法.
- 在制造业中对非静止性的原因进行分类.
- 为从业者开发一个三步机制 (变化检测,源-目标定义,方法选择).
主要成果:
- 识别和分析各种知识转移方法.
- 分类制造特定的非静止事件.
- 提出了一种实用机制,以指导选择用于制造数据分析的知识技术方法.
结论:
- 知识转移是一种可行的方法,可以减轻制造数据分析中的准确性退化.
- 拟议的机制支持从业者选择适合他们的特定制造挑战的KT方法.
- 这项工作有助于在制造业的研究和实践中采用知识转移.
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