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通过工业装备改造获得数据驱动的洞察力:具有机器学习用例的匿名数据集.
Daniele Atzeni1, Reshawn Ramjattan1, Roberto Figliè1
1Department of Computer Science, University of Pisa, 56126 Pisa, Italy.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
小型和中型企业 (SMEs) 现在可以利用机器学习 (ML) 来从设备数据中提取有价值的见解. 本研究提供了一个数据集,并展示了在中小企业中基于数据的决策的ML应用.
科学领域:
- 工业工程 工业工程 工业工程
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 中小企业 (SMEs) 在改装设备的数据分析方面面临挑战.
- 现有的研究不足以涵盖中小企业环境中的数据驱动洞察力.
研究的目的:
- 为数据驱动分析提供来自中小企业的匿名数据集.
- 展示机器学习 (ML) 技术的应用,以从有限的中小企业数据中提取见解.
主要方法:
- 从两个中型公司收集了7个月和1年的匿名电力消耗数据.
- 开发并应用了几个ML模型用于包括预测和分类在内的任务.
- 使用一种非侵入性和可扩展的数据收集程序.
主要成果:
- 证明了ML可以从有限的数据类型中提取短期内有用的信息.
- 成功地应用了ML模型用于电力消耗预测,项目分类,下一个机器状态预测和生产计数预测.
结论:
- 中小企业可以有效地利用有限的数据资源来利用ML技术.
- 该研究为中小企业提供了实用的见解,以利用可操作的数据见解.
- 这些发现有助于更好地理解在有限数据集的实际中小企业环境中使用ML的应用.
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