改进智能状态监控与诊断指标选择
Urszula Jachymczyk1, Paweł Knap1, Krzysztof Lalik1
1Faculty of Mechanical Engineering and Robotics, AGH University of Krakow, 30-059, Krakow, Poland.
Sensors (Basel, Switzerland)
|January 11, 2025
概括
风力轮机预测维护 (PdM) 的新特征选择方法有效地减少数据,同时保持高精度. 这种方法简化了模型,减少了缺陷检测和分类的计算时间.
科学领域:
- 工程 工程师 工程师 工程师
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 风力轮机的传统预测性维护 (PdM) 系统经常使用过度的振动衍生的功能,可能会降低性能.
- 一般的特征选择方法可能会误解或丢失PdM应用程序中的关键故障信息.
研究的目的:
- 开发一个结构化的特征选择方法,用于风力轮机叶片缺陷检测和分类.
- 通过减少特征维度来提高PdM系统的效率和可解释性.
主要方法:
- 提出了一种结合相关性分析和视觉评估的新型特征选择方法.
- 这种方法专注于保留特定领域的信息,与通用缩小维度的技术不同.
- 减少的功能集被用来训练机器学习 (ML) 和深度学习 (DL) 模型.
主要成果:
- 拟议的方法将状态指标的数量减少了大约3.27倍.
- 在缩小数据集上训练的机器学习模型在性能上与复杂的深度学习模型相匹配或超越.
- 预测的计算时间大大减少,复杂模型的计算时间高达50%,而不会影响ML模型的准确性.
结论:
- 一种结构化,与领域相关的特征选择方法通过简化数据输入和减少计算需求来增强PdM系统.
- 这种方法使得更简单的ML模型能够达到高精度,为工业应用提供可解释和具有成本效益的解决方案.
- 虽然DL模型指标没有得到类似的改进,但这项研究强调了优化特征集对高效PdM的价值.
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