用机器学习和人工智能模型优化离心的创新数据技术
Gaurav Sandeep Dave1, Amar Pradeep Pandhare1, Atul Prabhakar Kulkarni2
1Department of Mechanical Engineering, Sinhgad College of Engineering, Savitribai Phule Pune University, Pune, India.
PloS one
|June 10, 2025
概括
在离心机 (CPM) 中使用传感器融合进行高质量的数据采集,从而增强机器学习 (ML) 和人工智能 (AI) 模型. 这种数据处理将运营效率提高27.25%,并减少培训时间.
科学领域:
- 数据科学数据科学数据科学
- 机械工程 机械工程
- 机器学习 机器学习
背景情况:
- 现代离心机 (CPM) 需要强大的数据采集系统来监控性能.
- 数据质量对于机器学习 (ML) 和深度学习 (DL) 模型在分析CPM数据中的有效性至关重要.
- 传感器融合技术,以Dewesoft FFT DAQ系统为例,是从CPM中提取高保真数据的关键.
研究的目的:
- 突出数据清理,预处理和转换对于ML/AI模型利用的重要性.
- 详细介绍探索性数据分析 (EDA),数据可视化和特征工程 (FE) 等方法来增强数据.
- 证明验证数据在训练ML/DL模型中的应用,以优化CPM操作.
主要方法:
- 使用Dewesoft FFT DAQ系统与传感器融合来从CPM中获取数据.
- 实施数据清理,预处理,探索性数据分析 (EDA),数据可视化和功能工程 (FE).
- 应用假设测试来验证数据完整性,随后培训ML分类器和DL算法.
主要成果:
- 在运营效率方面实现了27.25%的提升,通过F1得分来衡量.
- 缩短了180秒的模型训练时间,使预测性维护速度更快.
- 使用精确度,回忆和F1得分指标证明了改进模型性能.
结论:
- 集成先进的数据科学技术显著提高了CPM运营效率和预测性维护能力.
- 在工业应用中,全面的数据预处理和验证对于可靠的ML/AI模型性能至关重要.
- 这种方法为明智的决策和离心操作优化提供了可操作的见解.
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