研究特征工程方法,用于域知识辅助轴承故障诊断
Christoph Bienefeld1,2, Florian Michael Becker-Dombrowsky1, Etnik Shatri1,2
1Institute for Product Development and Machine Elements, Technical University of Darmstadt, Otto-Berndt-Straße 2, 64287 Darmstadt, Germany.
Entropy (Basel, Switzerland)
|September 28, 2023
概括
这项研究通过将领域知识与特征工程和随机森林分类相结合,增强了滚动轴承故障诊断. 频段分离实现了高精度和效率,提供了比深度学习优越的可解释结果.
科学领域:
- 机械工程 机械工程
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 滚动轴承状况监测对于工业机械至关重要.
- 传统方法通常依赖于振动数据和机器学习进行故障诊断.
- 深度学习方法因其数据驱动的方法而越来越受欢迎,减少了对领域专业知识的需求.
研究的目的:
- 研究传统特征工程方法的有效性,结合滚动轴承故障诊断领域的知识.
- 评估信号处理技术和数学特征公式的新组合.
- 将特征工程方法的性能与纯粹数据驱动的深度学习方法进行比较.
主要方法:
- 一个全面的特征工程研究,涉及42个数学特征公式.
- 应用预处理方法,包括封面分析,实证模式分解,波形变换和频段分离.
- 使用随机森林分类器在CWRU带有故障数据集上的故障预测和评估.
主要成果:
- 使用频段分离的特征计算产生了特别高的预测准确度.
- 拟议的特征工程方法证明了高效率,低计算力度.
- 这种方法提供了与深度学习方法相比的卓越准确度,并具有可解释性的额外好处.
结论:
- 功能工程与领域知识相结合,为滚动轴承故障诊断提供了强大的和可解释的替代方案.
- 频段分离是这种应用的高效预处理技术.
- 与复杂的深度学习模型相比,提出的方法提供了一个具有竞争力和可解释性的解决方案.
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