摘要
弯曲振动与扭转振动耦合在旋转机械实际运行中往往不可避免。考虑不平衡转子不同复杂工况的弯扭耦合情况,利用深度学习技术的优势,构建了基于一维卷积神经网络的诊断模型,提出了一种用于处理不平衡转子发生弯曲,扭转以及弯扭耦合振动情况的智能故障诊断方法。分析了数据输入类型和L 2正则化对诊断的影响,优化了诊断模型以提高诊断精度,并进行了试验验证。研究结果表明,该方法可以实现不同转速下,发生弯扭耦合振动时单种或多种复合故障的智能诊断,获得比其他方法更好的诊断效果。
The coupling of bending vibration and torsional vibration often exists in the actual operation of rotating machinery.This paper considers the bending and torsional coupling of unbalanced rotors under different complex conditions,and utilizes the advantages of deep learning technology to construct a diagnosis model based on one-dimensional convolutional neural networks.An intelligent fault diagnosis method for handling the bending,torsion and bending torsional coupling vibration of the unbalanced rotors is proposed.The influence of data input type and L 2 regularization on the diagnosis is analyzed,and the diagnosis model is optimized to improve the diagnosis accuracy.The research results indicate that this method can realize intelligent diagnosis of single or multiple composite faults when bending torsional coupling vibration occurs at different speeds,and achieve better diagnostic results than other methods.
作者
李舜酩
陆建涛
沈涛
李香莲
LI Shunming;LU Jiantao;SHEN Tao;LI Xianglian(College of Energy and Power Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China;School of Automotive Engineering,Nantong Institute of Technology,Nantong 226002,China;Intelligent Automobile Department,Shanghai Huawei Technologies Co.,Ltd.,Shanghai 201206,China)
出处
《重庆理工大学学报（自然科学）》
北大核心
2023年第7期101-109,共9页
Journal of Chongqing University of Technology：Natural Science
基金
国家自然科学基金项目(51975262)。
关键词
转子系统
弯扭耦合振动
深度学习
L
2正则化
rotor system
bending and torsional coupled vibration
deep learning
L 2 regularization
