旋转机械的方便智能诊断:基于特征重建的改进深森林方法
Jiayu Chen1, Boqing Yao1, Cuiyin Lin1
1College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 210016, China; Civil Aviation Key Laboratory of Aircraft Health Monitoring and Intelligent Maintenance, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 211106, China.
本研究介绍了一种改进的深森林方法,用于在旋转机械中进行智能故障诊断,特别适用于有限数据的多个混合故障. 该方法增强了从振动数据中提取特征,提供了更强大,更实用的解决方案.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 机器学习 机器学习
背景情况:
- 深度学习为智能故障诊断提供了自动特征提取的优势.
- 复杂的超参数调整和有限的数据阻碍了实际应用,特别是旋转机械的多重混合故障.
研究的目的:
- 为旋转机械开发一种方便有效的智能故障诊断方法.
- 为了应对高计算成本,特征沉浸和故障诊断中小型训练样本的挑战.
主要方法:
- 为智能故障诊断提出了改进的深森林模型.
- 集成了特征重建算法,以处理长时间序列振动数据并减轻特征沉没.
- 该方法与基于深度神经网络的方法进行了验证.
主要成果:
- 提议的改进深森林方法在诊断故障方面表现出卓越的有效性.
- 该方法在各种超参数设置中显示了稳定性.
- 实验结果证实了对旋转机械故障诊断的传统深度学习方法的优越性.
结论:
- 改进的深森林方法为旋转机械的智能故障诊断提供了实用和强大的解决方案.
- 特性重建算法有效地解决了与振动数据和有限样本相关的挑战.
- 这种方法提高了深度学习在工业诊断中的适用性.
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