期刊文献+

基于改进AlexNet模型的滚动轴承故障诊断方法

Fault diagnosis method for rolling bearings based on improved AlexNet model
下载PDF
导出
摘要 针对传统方法难以提取滚动轴承故障特征、诊断精度不佳且模型结构复杂等问题,提出一种改进Alexnet模型的滚动轴承故障诊断方法。首先,将滚动轴承各故障振动信号转化为富含时频信息的二维特征图样本集,按一定比例划分为训练集与验证集;然后针对AlexNet模型中存在的模型训练速度慢、准确性不高等缺点进行改进,使用ImageNet图像数据集对改进模型进行训练,并保存训练过程获取的知识;最后将保存的训练信息迁移应用于改进模型对轴承故障数据集的诊断。通过改进前后模型对部分cifar-10图像数据集的训练与验证情况证明了改进模型的优化效果,对比常见网络模型对轴承10类别故障诊断情况,所提方法具有更好的诊断效率和诊断精度。 A rolling bearing fault diagnosis method based on an improved Alexnet model was proposed to address the issues of difficult feature extraction,poor diagnostic accuracy,and complex model structure in traditional methods.Firstly,the vibration signals of each fault of the rolling bearing were converted into a two-dimensional feature map sample set rich in time-frequency information,and divided into a training set and a validation set in a certain proportion.Then,improvements were made to the slow training speed and low accuracy of the Alexnet model.The ImageNet image dataset was used to train the improved model and save the knowledge obtained during the training process.Finally,the saved training information was transferred and applied to the improved model for diagnosing bearing faults in the dataset.The optimization effect of the improved model was demonstrated through training and validation on some cifar-10 image datasets before and after improvement.Compared with common network models for bearing 10 category fault diagnosis,the proposed method had better diagnostic efficiency and accuracy.
作者 华金榜 杨文军 程志林 温洪泉 曾良才 HUA Jinbang;YANG Wenjun;CHENG Zhilin;WEN Hongquan;ZENG Liangcai(School of Mechanical Automation,Wuhan University of Science and Technology,Wuhan 430081,Hubei,China;Key Laboratory of Metallurgical Equipment and Control Technology,Ministry of Education,Wuhan University of Science and Technology,Wuhan 430081,Hubei,China)
出处 《农业装备与车辆工程》 2024年第8期88-94,共7页 Agricultural Equipment & Vehicle Engineering
基金 国家重点研发计划(2021YFB2011200)。
关键词 滚动轴承 故障诊断 改进Alexnet模型 迁移学习 时频特征图 rolling bearings fault diagnosis improved AlexNet model transfer learning time frequency characteristic map
  • 相关文献

参考文献9

二级参考文献103

  • 1曾大有,王晓威,邓风茹.量子力学中的Heisenberg测不准原理的数学推导以及在小波分析中的应用[J].北华航天工业学院学报,2006(4):29-32. 被引量:3
  • 2李冬辉,刘浩.基于概率神经网络的故障诊断方法及应用[J].系统工程与电子技术,2004,26(7):997-999. 被引量:38
  • 3刘良顺,魏立东,宋希庚,薛冬新.基于RBF神经网络的滚动轴承故障诊断方法[J].农业机械学报,2006,37(3):163-165. 被引量:11
  • 4Paya B A, Esat I I .Artificaial Neural Network Based Fault Diagnostics of Rotating Machinery Using Wavelet Transforms sa a Preprocessor .Mechanical Systems and Signal Processing 1997 .11(5):751-765.
  • 5Huang N E, et al .The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlimear and Non-Stationary Time Series Analysis .Proc .R .Soc.Lond. A,1998,454:903-995.
  • 6YAN R,LIU Y,GAO R X.Permutation entropy:a nonlinear statistical measure for status characterization of rotary machines[J].Mechanical Systems and Signal Processing,2012,29:474-484.
  • 7CHRISTOPH B,BERND P.Permutation entropy:a natural complexity measure for time series[J].Physical Review Letters,2002,88(17):174102-1-4.
  • 8FRASER A M,SWINNEY H L.Independent coordinates for strange attractors from mutual information[J].Physical Review A,1986,33(2):1134-1140.
  • 9CAO L Y.Practical method for determining the minimum embedding dimension of a scalar time series[J].Physica D:Nonlinear Phenomena,1997,110(1/2):43-50.
  • 10SAPANKEVYCH N I,SANKAR R.Time series prediction using support vector machines:a survey[J].IEEE Computational Intelligence Magazine,2009,4 (2):25-38.

共引文献624

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部