离心故障诊断基于一个新的SobelEdge胆量图和CNN的错误诊断
Wasim Zaman1, Zahoor Ahmad1, Muhammad Farooq Siddique1
1Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of Korea.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
这项研究引入了一种使用信号处理和深度学习来诊断离心故障的新方法. 与现有技术相比,新型的SobelEdge扫描图提高了故障分类的准确性.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 离心在工业应用中至关重要.
- 准确的故障分类对于运营效率和安全至关重要.
- 现有的方法与振动信号中的噪声干扰作斗争.
研究的目的:
- 开发一个强大的框架来分类离心条件.
- 为了增强来自噪音振动信号的故障特定特征提取.
- 为了提高离心故障诊断的准确性.
主要方法:
- 获取和预处理离心振动信号.
- 应用斯托克威尔变换 (S-变换) 来生成刻度图.
- 引入Sobel过器以创建增强的"SobelEdge"刻度图. 引入Sobel过器以创建增强的"SobelEdge"刻度图.
- 使用卷积神经网络 (CNN) 进行故障分类,使用新的头图.
主要成果:
- 拟议的方法有效地减少了宏观结构和干扰噪声的影响.
- 索贝尔-埃奇扫描图 (SobelEdge scalograms) 比标准的S转换扫描图 (S-transform scalograms) 更清楚地突出了分辨性断层特征.
- 该CNN模型实现了卓越的离心故障分类性能.
结论:
- 新的框架显示了离心故障分类的显著改进.
- 结合S转换,索贝尔过和CNN,为状态监控提供了一种强大的方法.
- 这种方法为诊断离心中的持续情况提供了可靠的解决方案.
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