A new depth residual hedging network model is proposed, in which, with the help of Inception stacking idea, a stacked convolution hedging structural block was proposed to accelerate the convergence speed of the network, and a new identity mapping block is designed to realize the residual connection between the input layer and the middle layer. Moreover, the follow-up Squash function is introduced in the full connection layer to prevent the divergence of loss gradient. The depth residual offset network is applied to the fault diagnosis of the rolling bearing. In the preprocessing, the frequency spectrum of the vibration acceleration signal of the rolling bearing is directly taken as the input of the network, thus, simplifying the preprocessing of the data. Finally, two sets of actual rolling bearing failure data are used for methods validation, and to be compared with 18 Layer Residual Networks Deep Residual Networks (Resnet18), Convolutional Neural Networks (CNN) and other verification methods. The results show that of the depth of residual hedge network test accuracy of the model proposed is estimated at 2% more than other models, and training time can be shortened to one third, which fully suggests that the method has strong robustness and fast convergence rate.
[1] WANG R X, JIANG H K, LI X Q, et al. A reinforcement neural architecture search method for rolling bearing fault diagnosis[J]. Measurement, 2020, 154: 107417.
[2] 张向阳, 陈果, 郝腾飞, 等. 基于机匣信号的滚动轴承故障卷积神经网络诊断方法[J]. 航空动力学报, 2019, 34(12): 2729-2737. ZHANG X Y, CHEN G, HAO T F, et al. Convolutional neural network diagnosis method of rolling bearing fault based on casing signal[J]. Journal of Aerospace Power, 2019, 34(12): 2729-2737 (in Chinese).
[3] 王奉涛, 薛宇航, 王洪涛, 等. GLT-CNN方法及其在航空发动机中介轴承故障诊断中的应用[J]. 振动工程学报, 2019, 32(6): 1077-1083. WANG F T, XUE Y H, WANG H T, et al. GLT-CNN and its application of aero-engine intermediary bearing fault diagnosis[J]. Journal of Vibration Engineering, 2019, 32(6): 1077-1083 (in Chinese).
[4] WANG X, ZHENG Y, ZHAO Z Z, et al. Bearing fault diagnosis based on statistical locally linear embedding[J]. Sensors (Basel, Switzerland), 2015, 15(7): 16225-16247.
[5] 雷亚国, 贾峰, 孔德同, 等. 大数据下机械智能故障诊断的机遇与挑战[J]. 机械工程学报, 2018, 54(5): 94-104. LEI Y G, JIA F, KONG D T, et al. Opportunities and challenges of machinery intelligent fault diagnosis in big data era[J]. Journal of Mechanical Engineering, 2018, 54(5): 94-104 (in Chinese).
[6] ZHAO M H, ZHONG S S, FU X Y, et al. Deep residual networks with adaptively parametric rectifier linear units for fault diagnosis[J]. IEEE Transactions on Industrial Electronics, 2021, 68(3): 2587-2597.
[7] WANG F, JIANG H K, SHAO H D, et al. An adaptive deep convolutional neural network for rolling bearing fault diagnosis[J]. Measurement Science and Technology, 2017, 28(9): 095005.
[8] 雷亚国, 杨彬, 杜兆钧, 等. 大数据下机械装备故障的深度迁移诊断方法[J]. 机械工程学报, 2019, 55(7): 1-8. LEI Y G, YANG B, DU Z J, et al. Deep transfer diagnosis method for machinery in big data era[J]. Journal of Mechanical Engineering, 2019, 55(7): 1-8 (in Chinese).
[9] WEN L, GAO L, LI X Y. 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.
[10] GUO X J, CHEN L, SHEN C Q. Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis[J]. Measurement, 2016, 93: 490-502.
[11] LEI J H, LIU C, JIANG D X. Fault diagnosis of wind turbine based on long short-term memory networks[J]. Renewable Energy, 2019, 133: 422-432.
[12] SHAO H D, JIANG H K, ZHANG X, et al. Rolling bearing fault diagnosis using an optimization deep belief network[J]. Measurement Science and Technology, 2015, 26(11): 115002.
[13] WANG Y L, YANG H B, YUAN X F, et al. Deep learning for fault-relevant feature extraction and fault classification with stacked supervised auto-encoder[J]. Journal of Process Control, 2020, 92: 79-89.
[14] HUANG W Y, CHENG J S, YANG Y, et al. An improved deep convolutional neural network with multi-scale information for bearing fault diagnosis[J]. Neurocomputing, 2019, 359: 77-92.
[15] KHORRAM A, KHALOOEI M, REZGHI M. End-to-end CNN+LSTM deep learning approach for bearing fault diagnosis[J]. Applied Intelligence, 2021, 51(2): 736-751.
[16] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2016: 770-778.
[17] HE K M, ZHANG X Y, REN S Q, et al. Identity mappings in deep residual networks[M]//Computer Vision—ECCV 2016. Cham: Springer International Publishing, 2016: 630-645.
[18] ZHAO M H, ZHONG S S, FU X Y, et al. Deep residual shrinkage networks for fault diagnosis[J]. IEEE Transactions on Industrial Informatics, 2020, 16(7): 4681-4690.
[19] WEN L, LI X Y, GAO L. A transfer convolutional neural network for fault diagnosis based on ResNet-50[J]. Neural Computing and Applications, 2020, 32(10): 6111-6124.
[20] DU Y, WANG A M, WANG S, et al. Fault diagnosis under variable working conditions based on STFT and transfer deep residual network[J]. Shock and Vibration, 2020(1): 1274380.
[21] ZHU H G, WANG R, ZHANG X D. Image captioning with dense fusion connection and improved stacked attention module[J]. Neural Processing Letters, 2021, 53(2): 1101-1118.
[22] SABOUR S, FROSST N, HINTON G E. Dynamic routing between capsules[C]//Advances in Neural Information Processing Systems. Long Beach: NIPS, 2017: 3859-3869.
[23] SHAO H D, JIANG H K, ZHANG H Z, et al. Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing[J]. Mechanical Systems and Signal Processing, 2018, 100: 743-765.
[24] 昝涛, 王辉, 刘智豪, 等. 基于多输入层卷积神经网络的滚动轴承故障诊断模型[J]. 振动与冲击, 2020, 39(12): 142-149, 163. ZAN T, WANG H, LIU Z H, et al. A fault diagnosis model for rolling bearings based on a multi-input layer convolutional neural network[J]. Journal of Vibration and Shock, 2020, 39(12): 142-149, 163 (in Chinese).
CJA