摘要
为了有效捕获旋转机械振动信号中蕴含的故障特征,进而高效地完成故障诊断任务,设计了一种将二维特征图像和轻量化神经网络相结合的故障诊断模型。首先,将采集到的一维振动信号以改进的集成经验模态分解(Modified Ensemble Empirical Mode Decomposition,MEEMD)算法进行分解,得到本征模态函数(Intrinsic Mode Function,IMF)分量,并筛选相应的IMF分量进行求和重构,以增强振动信号的幅值波动,进而使得马尔科夫变迁场(Markov Transition Field,MTF)能够更为有效地表征重构信号中的故障特征;然后,将MTF生成的二维特征图像输入到残差深度可分离卷积神经网络(Residual Depth Separable Convolutional Neural Network,ResDSCNN)模型中,进行特征提取与故障诊断。使用行星齿轮箱故障数据集验证了模型性能。结果表明,该模型对于各类齿轮故障的诊断正确率可达98%以上。
In order to effectively capture the fault features contained in the vibration signals of the rotating machinery and complete the fault diagnosis task efficiently,a fault diagnosis model combining two-dimensional image features and lightweight neural network is designed.Firstly,the collected one-dimensional vibration signals are decomposed by modified ensemble empirical mode decomposition(MEEMD)to obtain the intrinsic mode function(IMF)components,and the corresponding IMF components are selected for sum reconstruction to enhance the amplitude fluctuation of vibration signals.Then,Markov transition field(MTF)could be used to more effectively characterize the fault features in the reconstructed signals.Secondly,the 2D feature map generated by MTF is input into residual depth separable convolutional neural network(ResDSCNN)for feature extraction and fault diagnosis.The planetary gearbox fault data set is used to verify the performance of the model,and the results show that the diagnosis accuracy of the model for all kinds of gear faults can reach more than 98%.
作者
胡孟楠
杨喜旺
黄晋英
胡宏俊
王成
Hu Mengnan;Yang Xiwang;Huang Jinying;Hu Hongjun;Wang Cheng(School of Big Data,North University of China,Taiyuan 030051,China;School of Mechanical Engineering,North University of China,Taiyuan 030051,China;Representative Office of Army Equipment Department in Beijing District,Beijing 100000,China)
出处
《机械传动》
北大核心
2024年第2期170-176,共7页
Journal of Mechanical Transmission
基金
山西省自然科学基金项目(201901D111157)
山西省重点研发计划(国际科技合作201903D421008)
山西省青年科技研究基金资助项目(201901D211202)。
