基于修改后的InceptionV3的电机故障诊断的深度学习方法.
Lifu Xu1, Soo Siang Teoh2, Haidi Ibrahim1
1School of Electrical and Electronic Engineering, USM Engineering Campus, Universiti Sains Malaysia, 14300, Nibong Tebal, Malaysia.
Scientific reports
|May 29, 2024
概括
这项研究介绍了一种先进的热像方法,用于电机故障检测,使用InceptionV3模型与Squeeze-and-Excitation (SE) 注意力机制. 该技术在识别各种电机故障方面实现了高精度,增强了工业诊断.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 电机在许多行业中至关重要,但由于运行压力和维护不良,容易发生故障.
- 早期和准确的故障检测对于防止昂贵的停机时间和确保运营安全至关重要.
研究的目的:
- 开发和评估一种基于热图的新方法,用于检测电机故障.
- 通过使用深度学习来提高运动故障诊断的准确性和效率.
主要方法:
- 一种基于温度的方法,使用InceptionV3深度学习模型.
- 应用对比限度自适应直方体平衡 (CLAHE) 进行图像增强.
- 整合一个Squeeze-and-Excitation (SE) 通道注意力机制,以提高InceptionV3的性能.
- 使用369个热图像的数据集,涵盖11种断层类型,增强数据大小.
- 采用五重交叉验证来进行可靠的评估.
- 使用InceptionV3进行特征提取的替代方法,并与支持矢量机 (SVM) 进行分类.
主要成果:
- 带有SE机制的拟议InceptionV3实现了高性能指标:98.82%的准确性,98.93%的精度,98.82%的回忆率和98.87%的F1得分.
- 混合InceptionV3-SVM模型在所有评估指标上展示了完美的100%检测率.
- 这些方法在从热图像中分类各种电机故障方面被证明是有效的.
结论:
- 开发的基于温度的深度学习方法,特别是带有SE关注的InceptionV3,显著提高了电机故障检测.
- 将深度学习特征提取与SVM等传统分类器相结合,为工业电机故障诊断提供了高度准确和强大的解决方案.
- 这项研究为预测性维护和提高电机系统可靠性提供了有价值的工具.
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