一个新的深度卷积神经网络,结合全球特征提取和详细特征提取,用于轴承复合故障诊断
Shuzhen Han1,2, Pingjuan Niu1,3, Shijie Luo2
1School of Mechanical Engineering, Tiangong University, Tianjin 300387, China.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
这项研究引入了一种新型的深卷积神经网络 (CNN),用于轴承复合故障诊断,达到98%以上的准确性. GDDCNN模型增强了特征提取,以实现更稳定,更可靠的状态监控.
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:
- 轴承故障在旋转机械中至关重要,导致意外停机.
- 由于复杂的信号模式,轴承中精确的复合故障诊断具有挑战性.
研究的目的:
- 开发一种新的深卷积神经网络 (CNN),用于增强轴承化合物故障诊断.
- 改进从原始振动信号中自动提取故障特征.
主要方法:
- 提出了一种新的深度卷积神经网络 (GDDCNN),集成全球和详细的特征提取.
- 为了全面的特征捕获,在不同的层中利用了宽和小的内核大小.
- 整合了修改的激活层 (CReLU) 和GMP层来增强功能稳定性.
主要成果:
- 在两个轴承数据集上,GDDCNN模型在复合故障诊断中实现了超过98%的准确性.
- 与其他三种基于CNN的方法相比,表现出卓越的稳定性和性能.
- 成功地从复杂的原始信号中提取有效的特征,以进行准确的诊断.
结论:
- 拟议的GDDCNN为轴承复合故障诊断提供了一个高度准确和稳定的解决方案.
- GDDCNN的自动特征提取功能显著提高了诊断性能.
- 这种方法对先进的状态监测和机械设备的预测性维护具有前景.
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