面向旋转机械迁移诊断的分层并行网络模型自动创建方法

doi:10.3901/JME.2022.22.115

机械工程学报 ›› 2022, Vol. 58 ›› Issue (22): 115-128.doi: 10.3901/JME.2022.22.115

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面向旋转机械迁移诊断的分层并行网络模型自动创建方法

周健^1,2, 郑联语^1,2, 王艺玮^1,2, 王移川^3,4

1. 北京航空航天大学机械工程及自动化学院北京 100191;
2. 航空高端装备智能制造技术工业和信息化部重点实验室北京 100191;
3. 北京精密机电控制设备研究所北京 100076;
4. 航天伺服驱动与传动技术实验室北京 100076

收稿日期:2022-02-07 修回日期:2022-06-09 出版日期:2022-11-20 发布日期:2023-02-07
通讯作者: 王艺玮(通信作者),女,1988年出生,博士,讲师,博士研究生导师。主要研究方向为工业智能,复杂装备智能运维系统,设备故障诊断,剩余寿命预测与健康管理。E-mail:wangyiwei@buaa.edu.cn
作者简介:周健,男,1994年出生,博士研究生。主要研究方向为数据驱动的设备诊断与退化预测,设备健康管理,工业智能。E-mail:zhouj@buaa.edu.cn
基金资助:
国家重点研发计划(2020YFB1708400);航天伺服驱动与传动技术实验室开发基金(LASAT-2021-01);国家自然科学基金(51805262)资助项目

Automatic Model Creation Method of Hierarchical Parallel Network Model for Transfer Diagnosis of Rotating Machinery

ZHOU Jian^1,2, ZHENG Lian-yu^1,2, WANG Yi-wei^1,2, WANG Yi-chuan^3,4

1. School of Mechanical Engineering and Automation, Beihang University, Beijing 100191;
2. MIIT Key Laboratory of Intelligent Manufacturing Technology for Aeronautics Advanced Equipments, Ministry of Industry and Information Technology, Beijing 100191;
3. Beijing Institute of Precision Mechatronics Control Equipment, Beijing 100076;
4. Aerospace Servo Drive and Transmission Technology Laboratory, Beijing 100076

Received:2022-02-07 Revised:2022-06-09 Online:2022-11-20 Published:2023-02-07

摘要/Abstract

摘要： 针对当前基于深度学习的旋转机械故障诊断方法存在的依赖人工建模经验、需手动调参、试错迭代、面对不同诊断任务需重新创建诊断模型、异构迁移性差等问题,文中提出一种面向旋转机械迁移诊断的分层并行网络模型自动创建方法,可根据不同诊断任务快速自动地搜索出具有异构迁移性能的高精度诊断模型。基于神经结构搜索(Neural architecture search,NAS)与模块化设计的思想,设计了两类包含多层网络并行结构的基础块,区别于逐网络层搜索的模式,以基础块为单位进行搜索提高效率,控制器输出决策序列确定基础块的内部结构,并将其堆叠形成分层并行结构的子模型,根据子模型在诊断任务上的验证结果利用策略梯度算法优化控制器,循环迭代上述过程不断提高子模型的诊断精度。子模型的分层并行结构支撑了其良好的异构迁移性能,此外为解决NAS搜索耗时的瓶颈问题,在子模型训练过程中设置了权值共享机制以提高自动建模效率。所提方法面向四个不同旋转机械故障数据集进行自动建模和异构迁移诊断试验,结果表明针对四个不同诊断任务,所提方法均能高效创建出100%精度的诊断模型,消耗时间313s到1601s不等,并且所创建的子模型在仅用10%目标域数据耗费100s时间进行微调的条件下,即可面对目标诊断任务达到95%以上的迁移诊断精度。

关键词: 旋转机械, 分层并行网络, 模型自动创建, 权值共享, 迁移诊断

Abstract: In view of the current deep learning-based fault diagnosis methods of rotating machinery relying on manual modeling experience, requiring manual parameter adjustment and continuous trial-and-error iteration, re-creation of diagnosis models for different diagnostic tasks, an automatic creation method of hierarchical parallel network model for transfer diagnosis of rotating machinery is proposed, which can quickly and automatically search high precision diagnosis models with heterogeneous transfer performance according to different diagnosis tasks. Based on neural architecture search(NAS) and modular design ideas, two types of foundation blocks of parallel structure containing multiple layers are designed, which is different from the traditional NAS method to search layer by layer, but searches based on the foundation blocks. The controller outputs decision sequence to determine the foundation blocks' structure and stacks them to form a hierarchical parallel candidate model. Then according to the verification results of the candidate model on the diagnosis task, the controller is optimized using the strategy gradient algorithm, and the diagnosis accuracy of the candidate model is continuously improved by iterating the above process. The hierarchical parallel structure of the candidate model supports its good heterogeneous transfer performance. In addition, in order to solve the time-consuming bottleneck problem of NAS method, the weight sharing mechanism is set in the candidate model training process to improve the efficiency of automatic modeling. The proposed method is used to conduct automatic modeling and heterogeneous transfer diagnosis experiments for four different rotating machinery fault datasets, and the results show that the proposed method can efficiently create 100% accuracy diagnosis models for four different diagnosis tasks, consuming 313s to 1 601 s, and the candidate model can achieve a transfer diagnosis accuracy of more than 95% for the target diagnosis task with only 10% of the target domain data and 100s of fine-tuning.

Key words: rotating machinery, hierarchical parallel network, automatic model creation, weight sharing, transfer diagnosis

中图分类号:

TH165
TG506

周健, 郑联语, 王艺玮, 王移川. 面向旋转机械迁移诊断的分层并行网络模型自动创建方法[J]. 机械工程学报, 2022, 58(22): 115-128.

ZHOU Jian, ZHENG Lian-yu, WANG Yi-wei, WANG Yi-chuan. Automatic Model Creation Method of Hierarchical Parallel Network Model for Transfer Diagnosis of Rotating Machinery[J]. Journal of Mechanical Engineering, 2022, 58(22): 115-128.

参考文献

[1] 刘东东,程卫东,万广通.基于故障特征趋势线模板的滚动轴承故障诊断[J].机械工程学报,2017,53(9):83-91.LIU Dongdong,CHENG Weidong,WAN Guangtong.Bearing fault diagnosis based on fault characteristic trend template[J]. Journal of Mechanical Engineering,2017,53(9):83-91.
[2] DHAMANDE L S, CHAUDHARI M B. Compound gear-bearing fault feature extraction using statistical features based on time-frequency method[J].Measurement,2018,125:63-77.
[3] YAN Ruqiang,GAO R X. Energy-based feature extraction for defect diagnosis in rotary machines[J]. IEEE Trans.Instrum. Meas.,2019,58(9):3130-3139.
[4] 黄包裕,张永祥,赵磊.基于布谷鸟搜索算法和最大二阶循环平稳盲解卷积的滚动轴承故障诊断方法[J].机械工程学报,2021,57(9):99-107.HUANG Baoyu, ZAHNG Yongxiang, ZHAO Lei.Research on fault diagnosis method of rolling bearings based on cuckoo search algorithm and maximum second order cyclostationary blind deconvolution[J]. Journal of Mechanical Engineering,2021,57(9):99-107.
[5] 姚德臣,杨建伟,程晓卿,等.基于多尺度本征模态排列熵和SA-SVM的轴承故障诊断研究[J].机械工程学报,2018,54(9):168-176.YAO Dechen,YANG Jianwei,CHENG Xiaoqing,et al.Railway rolling bearing fault diagnosis based on multi-scale IMF permutation entropy and SA-SVM classifier[J]. Journal of Mechanical Engineering,2018,54(9):168-176.
[6] 胡茑庆,陈微鹏,程哲,等.基于经验模态分解和深度卷积神经网络的行星齿轮箱故障诊断方法[J].机械工程学报,2019,55(7):9-18.HU Niaoqing,CHEN Weipeng,CHENG Zhen,et al. Fault diagnosis for planetary gearbox based on EMD and deep convolutional neural networks[J]. Journal of Mechanical Engineering,2019,55(7):9-18.
[7] 周兴康,余建波.基于深度一维残差卷积自编码网络的齿轮箱故障诊断[J].机械工程学报,2020,56(7):96-108.ZHOU Xingkang,YU Jianbo. Gearbox fault diagnosis based on one-dimension residual convolutional auto-encoder[J]. Journal of Mechanical Engineering,2020,56(7):96-108.
[8] 董绍江,裴雪武,吴文亮,等.基于多层降噪技术及改进卷积神经网络的滚动轴承故障诊断方法[J].机械工程学报,2021,57(1):148-156.DONG Shaojiang,PEI Xuewu,WU Wenliang,et al.Rolling bearing fault diagnosis method based on multilayer noise reduction technology and improved convolutional neural network[J]. Journal of Mechanical Engineering,2021,57(1):148-156.
[9] WANG Yiwei, ZHOU Jian, ZHENG Lianyu. An end-to-end fault diagnostics method based on convolutional neural network for rotating machinery with multiple case studies[J]. Journal of Intelligent Manufacturing,2020,33:809-830.
[10] 沈长青,汤盛浩,江星星,等.独立自适应学习率优化深度信念网络在轴承故障诊断中的应用研究[J].机械工程学报,2019,55(7):81-88.SHEN Changqing, TANG Shenghao, JIANG Xingxing,et al. Bearings fault diagnosis based on improved deep belief network by self-individual adaptive learning rate[J]. Journal of Mechanical Engineering,2019,55(7):81-88.
[11] LI Xiang,ZHANG Wei,DING Qian. A robust intelligent fault diagnosis method for rolling element bearings based on deep distance metric learning[J]. Neurocomputing,2018,310:77-95.
[12] WEN Long,GAO Liang,LI Xinyu. A new deep transfer learning based on sparse auto-encoder for fault diagnosis[J]. IEEE Transactions on Systems,Man,and Cybernetics:Systems,2019,49(1):136-144.
[13] ZHOU Tianyi,PAN Jialin,TSANG W I,et al. A deep learning framework for hybrid heterogeneous transfer learning[J]. Artificial Intelligence,2019,275:310-328.
[14] GUO Liang,LEI Yaguo,XING Saibo,et al. Deep convolutional transfer learning network:a new method for intelligent fault diagnosis of machines with unlabeled data[J]. IEEE Transactions on Industrial Electronics,2019,66(9):7316-7325.
[15] ZHOU Jian,ZHENG Lianyu,WANG Yiwei. A multistage deep transfer learning method for machinery fault diagnostics across diverse working conditions and devices[J]. IEEE Access,2020,8:80879-80898.
[16] BAKER B,GUPTA O,NAIK N,et al. Designing neural network architecture using reinforcement learning[C/CD]//Proc. Int. Conf. Learn. Representations,2017.
[17] REAL E,MOORE S,SELLE A,et al. Large-scale evolution of image classifiers[C]//Proceedings of the 34th International Conference on Machine Learning,PMLR,2017,70:2902-2911.
[18] WANG Ruixin,JIANG Hongkai,LI Xingqiu,et al. A reinforcement neural architecture search method for rolling bearing fault diagnosis[J]. Measurement,2020,154:107417.
[19] JIE Cao,MA Jialin,HUANG Dailin,et. al. Finding the optimal multilayer network structure through reinforcement learning in fault diagnosis[J].Measurement,2022,188:110377.
[20] ZHOU Zheng,LI Tianfu,ZHANG Zilong,et al. Bayesian differentiable architecture search for efficient domain matching fault diagnosis[J]. IEEE Transactions on Instrumentation and Measurement,2021,70:1-11.
[21] ZHANG Kaiyu,CHEN Jinglong,HE Shuilong,et. al.Differentiable neural architecture search augmented with pruning and multi-objective optimization for time-efficient intelligent fault diagnosis of machinery[J].Mechanical Systems and Signal Processing,2021,158:107773.
[22] ZHONG Zhao,YAN Junjie,WU Wei,et al. Practical block-wise neural network architecture generation[C/CD]//Proc. IEEE Conf. Comput. Vis. Pattern Recognit.,2018:2423-2432.
[23] LIU Hanxiao,SIMONYAN K,VINYALS O,et al.Hierarchical representations for efficient architecture search[C/OL]//ICLR 2018,arXiv:1711.00436.
[24] SZEGEDY C,LIU Wei,JIA Yangqing,et al. Going deeper with convolutions[C/CD]//2015 IEEE Conference on Computer Vision and Pattern Recognition,7-12,June,2015,Boston,MA.
[25] SZEGEDY C, LOFFE S, VANHOUCKE V, et al.Inception-v4,Inception-ResNet and the impact of residual connections on learning[C/CD]//2016 IEEE Conference on Computer Vision and Pattern Recognition,27-30 June,2016,Las Vegas,NV,USA.
[26] SZEGEDY C, VANHOUCKE V, LOFFE S, et al.Rethinking the inception architecture for computer vision[C/CD]//2016 IEEE Conference on Computer Vision and Pattern Recognition,27-30 June,2016,Las Vegas,NV,USA.

面向旋转机械迁移诊断的分层并行网络模型自动创建方法

Automatic Model Creation Method of Hierarchical Parallel Network Model for Transfer Diagnosis of Rotating Machinery

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