基于监督对比学习的蒸汽轮机叶片裂纹缺陷的分类和识别的研究
Qinglei Zhang1, Laifeng Tang1, Jiyun Qin1
1China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306, China.
Entropy (Basel, Switzerland)
|November 27, 2024
概括
这项研究引入了一种使用一维卷积神经网络 (1DCNN) 和通道注意力机制 (CAM) 的新方法,以准确检测蒸汽轮机叶片裂. 这种先进的技术显著提高了关键旋转设备的故障分类准确性.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 由于恶劣的操作条件,蒸汽轮机叶片的故障 (裂,断裂) 对设备的安全性和可靠性构成重大风险.
- 使用振动信号准确分类旋转叶片故障是具有挑战性的,因为故障特征的微妙差异.
研究的目的:
- 开发蒸汽轮机叶片的先进故障分类方法.
- 为了提高检测刀片裂和其他故障的准确性和可靠性.
主要方法:
- 结合一维卷积神经网络 (1DCNN) 进行本地特征提取,并使用通道注意力机制 (CAM) 突出关键特征.
- 包含一个投影头来规范特征空间,提高辨别能力.
- 使用监督对比学习 (SCL) 策略来捕捉微妙的故障差异.
主要成果:
- 在三个不同速度的多个裂纹断层类型中实现了高分类准确率99.61%,97.48%和96.22%.
- 与多层感知器 (MLP),残余网络 (ResNet),动量对比 (MoCo) 和变压器方法相比,表现出卓越的性能.
结论:
- 建议使用投射头和SCL的1DCNN-CAM方法有效地对蒸汽轮机叶片故障进行分类.
- 这种方法为检测旋转机械中的关键故障提供了显著的准确性改进.
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