基于VMD-1DCNN-GRU的轴承故障诊断  

Bearing Fault Diagnosis Based on VMD-1DCNN-GRU

作　　者：宋金波[1,2] 刘锦玲 闫荣喜 王鹏 路敬祎[1,2,3] SONG Jinbo;LIU Jinling;YAN Rongxi;WANG Peng;LU Jingyi(College of Electrical and Information Engineering,Northeast Petroleum University,Daqing 163318,China;Artificial Intelligence Energy Research Institute,Northeast Petroleum University,Daqing 163318,China;Sanya Offshore Oil&Gas Research Institute,Northeast Petroleum University,Sanya 572025,China)

机构地区：[1]东北石油大学电气信息工程学院,黑龙江大庆163318 [2]东北石油大学人工智能能源研究院,黑龙江大庆163318 [3]东北石油大学三亚海洋油气研究院,海南三亚572024

出　　处：《吉林大学学报(信息科学版)》2025年第1期34-42,共9页Journal of Jilin University(Information Science Edition)

基　　金：国家自然科学基金资助项目(61873058,62103096);海南省科技专项基金资助项目(ZDYF2022SHFZ105);海南省自然科学基金资助项目(623MS071);春晖计划基金资助项目(HZKY20220314);黑龙江省自然科学基金联合引导基金资助项目(LH2022F009);黑龙江省高校基本科研业务费基金资助项目(2023YDL-18)。

摘　　要：针对滚动轴承信号含噪声导致诊断模型训练困难的问题,提出了一种基于变分模态分解(VMD:Variational Mode Decomposition)和深度学习相结合的轴承故障诊断模型。首先,该方法通过VMD对轴承信号进行模态分解,并且通过豪斯多夫距离(HD:Hausdorff Distance)完成去噪,尽可能保留原始信号的特征。其次,将选择的有效信号输入一维卷积神经网络(1DCNN:1D Convolutional Neural Networks)和门控循环单元(GRU:Gate Recurrent Unit)相结合的网络结构(1DCNN-GRU)中完成数据的分类,实现轴承的故障诊断。通过与常见的轴承故障诊断方法比较,所提VMD-1DCNN-GRU模型具有最高的准确性。实验结果验证了该模型对轴承故障有效分类的可行性,具有一定的研究意义。Rolling bearing is one of the key components in rotating machinery,and long-term mechanical operation leads to wear easily.Traditional fault diagnosis relies on feature extraction,but due to loud noise during mechanical operation,effective signals are drowned.And the fault diagnosis network structure is complicated and there are too many parameters.Therefore,a bearing fault diagnosis model based on variational mode decomposition and deep learning is proposed for bearing wear detection.Firstly,the bearing signal is decomposed by VMD(Variational Mode Decomposition) and denoised by Hausdorff distance.Secondly,the selected effective signals are inputted into the network structure of one-dimensional convolutional neural network and gate recurrent unit to complete the classification of data and realize the fault diagnosis of bearings.Compared to common bearing fault diagnosis methods,the proposed VMD-1DCNN-GRU(Variational Mode Decomposition-1D Convolutional Neural Networks-Gate Recurrent Unit) model has the highest accuracy.The experimental results verify the feasibility of the proposed model for the effective classification of bearing faults,which has certain research significance.

关 键 词：故障诊断 深度学习 变分模态分解 一维卷积神经网络 门控循环单元 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TE973[自动化与计算机技术—控制科学与工程]