[1]刘志宇,黄亦翔.基于深度学习和迁移学习的液压泵健康评估方法[J].机械与电子,2018,(09):67-71.
　LIU Zhiyu,HUANG Yixiang.Health Assessment for Hydraulic Pump Based on Deep Learning and Transfer Learning[J].Machinery & Electronics,2018,(09):67-71.
点击复制

基于深度学习和迁移学习的液压泵健康评估方法()
分享到：

机械与电子[ISSN:1001-2257/CN:52-1052/TH]

卷:
期数:
2018年09期
页码:
67-71
栏目:
自动控制与检测
出版日期:
2018-09-24

文章信息/Info

Title:
Health Assessment for Hydraulic Pump Based on Deep Learning and Transfer Learning
文章编号:
1001-2257（2018）09-0067-05
作者:
刘志宇; 黄亦翔
(上海交通大学机械与动力工程学院,上海 200240)
Author(s):
LIU Zhiyu;  HUANG Yixiang
(School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)
关键词:
健康评估; 深度学习; 迁移学习; 卷积神经网络; 液压泵
Keywords:
health assessment;  deep learning;  transfer learning;  convolutional neural network;  hydraulic pump
分类号:
TH137.51；TP181
文献标志码:
A
摘要:
对液压泵建立健康评估模型需要大量训练数据，然而由于其工作条件随时间的地点变化，使得获取特定条件下的数据比较困难。为了在目标数据不足的条件下对液压泵建立健康评估模型，提出了一种深度学习和迁移学习的液压泵健康评估方法。首先，通过卷积神经网络的方法对已有大量历史条件下液压泵振动的频域信号建立预测模型，再用迁移学习的思想在少量目标液压泵数据上对深度学习模型进行微调。实验结果表明，该方法可以有效地提高预测准确率。
Abstract:
Building health assessment model for plunger pump needs a large amount of training data, whereas in the real world, working conditions changes all the time, which causes difficulties to obtain data in certain conditions.?In order to build an effective prediction model for health assessment for hydraulic pump with limited target data, this paper proposes a deep learning and transfer learning method for health assessment for hydraulic pump.?First, convolutional neural network (CNN) was applied to train a deep learning prediction model for the large number of existing historical frequency-domain data of vibration of hydraulic pump. Next, transfer learning was used to fine tune the CNN model with limited target data.?The experiment shows that this method can effectively improve the accuracy of prediction.

参考文献/References:

[1].沈美杰,赵龙章,周兵,等.基于PSO优化的RBF网络液压泵故障诊断研究[J].液压与气动,2016(5):87-92.
[2].Wang Z, Wang Z.Chaotic Parallel Support Vector Machine and its application for fault diagnosis of hydraulic pump[C]//Prognostics and Health Management.IEEE,2013:1-6.
[3].林杨,高思煜,刘同舜,等.基于深度学习的高速铣削刀具磨损状态预测方法[J].机械与电子,2017,35(07):12-17.
[4].孙文珺,邵思羽,严如强.基于稀疏自动编码深度神经网络的感应电动机故障诊断[J].机械工程学报,2016,52(9):65-71.
[5].Lv F, Wen C, Bao Z, et al.Fault diagnosis based on deep learning[C]//American Control Conference. American Automatic Control Council (AACC), 2016:6851-6856.
[6].赵光权,葛强强,刘小勇,等.基于DBN的故障特征提取及诊断方法研究[J].仪器仪表学报,2016,37(09):1946-1953.
[7].Liu R,Meng G,Yang B,et al.Dislocated Time Series Convolutional Neural Architecture: An Intelligent Fault Diagnosis Approach for Electric Machine[J].IEEE Transactions on Industrial Informatics,2017，13(3):1310-1320 .
[8].Dai W,Yang Q,Xue G R,et al.Boosting for transfer learning[C]//International Conference on Machine learning.ACM,2007，238（6）:193-200.
[9].Irie G,Asami T,Tarashima S,et al.Cross-modal transfer with neural word vectors for image feature learning[C]//Acoustics, Speech and Signal Processing (ICASSP),2017 IEEE International Conference on Acoustics,2017:2916-2920.
[10].Akilan T,Wu Q M J,Yang Y,et al.Fusion of transfer learning features and its application in image classification[C]//Electrical and Computer Engineering (CCECE), 2017 IEEE 30th Canadian Conference on. IEEE, 2017: 1-5.
[11].Shen F,Chen C,Yan R,et al.Bearing fault diagnosis based on SVD feature extraction and transfer learning classification[C]//Prognostics and System Health Management Conference (PHM),2015. IEEE, 2015:1-6.

相似文献/References:

[1]王骁贤,张保华,陆思良.基于连续小波变换和卷积神经网络的无刷直流电机故障诊断[J].机械与电子,2018,(06):29.
　WANG Xiaoxian,ZHANG Baohua,LU Siliang.Fault Diagnosis of Brushless Direct Current Motor Based on Continuous Wavelet Transform and Convolutional Neural Network[J].Machinery & Electronics,2018,(09):29.
[2]肖倩宏,康 鹏,杜 江,等.深度学习在电网智能调控系统中应用研究[J].机械与电子,2021,(01):38.
　XIAO Qianhong,KANG Peng,DU Jiang,et al.Research on the Application of Deep Learning Theory in Power Grid Intelligent Dispatching[J].Machinery & Electronics,2021,(09):38.
[3]许 哲,张少帅,郭 璐,等.无人机深度学习去雾算法[J].机械与电子,2021,(04):13.
　XU Zhe,ZHANG Shaoshuai,GUO Lu,et al. Deep Learning Defogging Algorithm for UAV[J].Machinery & Electronics,2021,(09):13.
[4]齐爱玲１,李 琳１,朱亦轩２,等.基于融合特征的双通道CNN滚动轴承故障识别[J].机械与电子,2021,(05):15.
　QI Ailing,LI Lin,ZHU Yixuan,et al.Dual Channel CNN Bearing Fault Identification Based on Fusion Feature[J].Machinery & Electronics,2021,(09):15.
[5]徐先峰,郑少杰,赵 依,等.基于数据分解与重构的光伏发电功率超短期预测[J].机械与电子,2022,(04):20.
　XU Xianfeng,ZHENG Shaojie,ZHAO Yi,et al.Ultra-short-term Prediction of Photovoltaic Power Generation Based on Data Decomposition and Deconstruction[J].Machinery & Electronics,2022,(09):20.
[6]江 励,熊达明,汤健华,等.自然光线环境中的空间物体快速识别和定位算法研究[J].机械与电子,2022,(06):8.
　JIANG Li,XIONG Daming,TANG Jianhua,et al.Recognition and Positioning Algorithm of Space Objects in Natural Light Environment[J].Machinery & Electronics,2022,(09):8.
[7]王西志,管声启,张理博,等.基于视觉引导的工业棒材上料系统研究[J].机械与电子,2023,41(05):19.
　WANG Xizhi,GUAN Shengqi,ZHANG Libo,et al.Research on Industrial Bar Feeding System Based on Visual Guidance[J].Machinery & Electronics,2023,41(09):19.
[8]王 青,吕绪山,党 帅,等.基于深度学习的纱管识别方法研究[J].机械与电子,2023,41(12):20.
　WANG Qing,LYU Xushan,DANG Shuai,et al.Research on Yarn Bobbin Detection Method Based on Deep Learning[J].Machinery & Electronics,2023,41(09):20.
[9]姜越夫,王 青,吕绪山.改进 YOLOv5s 的纱管目标检测方法[J].机械与电子,2024,42(02):29.
　JIANG Yuefu,WANG Qing,LYU Xushan.Improved YOLOv5s Method for Yarn Tube Object Detection[J].Machinery & Electronics,2024,42(09):29.
[10]谭继勇,罗 俊,谢江涛,等.基于鲸鱼优化和批量规范化卷积神经网络的振动信号去噪[J].机械与电子,2024,42(04):3.
　TAN Jiyong,LUO Jun,XIE Jiangtao,et al.A Convolutional Neural Network with Whale Optimization and Batch Normalization for the Denoising of Vibration Signal[J].Machinery & Electronics,2024,42(09):3.

备注/Memo

备注/Memo:
收稿日期：2018-04-24
作者简介：刘志宇（1993—），男，辽宁朝阳人，硕士研究生，主要研究方向为设备故障诊断与健康评估；黄亦翔（1980—），男，上海人，硕士研究生导师，助理研究员，主要研究方向为工业大数据分析、设备故障诊断与寿命预测等。
更新日期/Last Update: 2019-11-01