一个新的深度学习框架用于检测旋转轴的不平衡
1Departement of Mechanical Convergence Engineering, Hanyang University, 222, Wangsimni ro, Seongdong gu, Seoul 04763, Republic of Korea.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
一个新的深度学习 (DL) 算法有效地检测工业机器中的旋转器不平衡. 这种先进的方法可以准确地识别平衡/不平衡状态,并分类多个不平衡水平,提高机器的安全性和效率.
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:
- 转子不平衡是工业机器振动的主要原因,导致效率降低,部件损坏和潜在故障.
- 现有的轮子不平衡分类算法对于不同数量的类缺乏灵活性.
- 需要一个强大的多类预测算法来实时分类转子失衡.
研究的目的:
- 开发一种新的深度学习 (DL) 算法,用于检测旋转轴不平衡.
- 为了实现二进制 (平衡与不平衡) 和多类 (不平衡水平) 识别.
- 为工业旋转机械创建一个强大的实时故障检测机制.
主要方法:
- 开发了一个DL算法,集成ResNet和卷积神经网络 (CNN) 来进行特征提取.
- 利用加速度计振动传感器数据,使用快速里叶变换 (FFT) 和短时间里叶变换 (STFT) 进行预处理.
- 雇佣STFT因为其丰富的特征,提高模型性能.
主要成果:
- 开发的DL模型实现了对二进制分类 (平衡与不平衡) 的测试准确率为99.23%.
- 该模型显示了95.15%的测试准确度,用于对不平衡水平的多类分类.
- 拟议的算法表现优于独立的ResNet和CNN模型.
结论:
- 新的DL框架对于二进制和多类转子不平衡分类都是强大的.
- 该算法为工业旋转机械的实时故障检测提供了可靠的解决方案.
- 这种方法提高了操作安全性,并延长了关键机器部件的寿命.
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