[1] Guo L, Lei Y G, Xing S B, 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.

[2] JiaF, Lei YG, LinJ, et al. and intelligent diagnosis of rotating machinery with massive data[J]. Mechanical Systems and SignalProcessing, 2016, 72/73: 303- 315.

[3] 周围, 梁琦. 非平稳信号实时谱分析算法及其FPGA实现[J]. 重庆邮电大学学报(自然科学版), 2018, 30(5): 633-641.

    Zhou W, Liang Q. Real-time spectrum analysis algorithm for non-stationary signal and it's implementation on FPGA platforms[J]. Journal of Chongqing University of Posts and Telecommunications (Nature Science Edition), 2018, 30(5): 633-641.

[4] 任学平, 李攀, 王朝阁, 等. 基于改进VMD与包络导数能量算子的滚动轴承早期故障诊断[J]. 振动与冲击, 2018, 37(15): 6-13.

    Ren X P, Li P, Wang C G, et al. Early fault diagnosis of rolling bearings based on improved VMD and envelope derivative energy operator[J]. Journal of Vibration and Shock, 2018, 37(15): 6-13.

[5] 王奉涛, 柳晨曦, 张涛, 等. 基于k值优化VMD的滚动轴承故障诊断方法[J]. 振动、测试与诊断, 2018, 38(3): 540-547.

    Wang F T, Liu C X, Zhang T, et al. Rolling bearing fault diagnosis method based on k-value optimized VMD[J]. Journal of Vibration, Measurement & Diagnosis, 2018, 38(3): 540-547.

[6] Hoang D T, Kang H J. Rolling element bearing fault diagnosis using convolutional neural network and vibration image[J]. Cognitive Systems Research, 2019, 53: 42-50.

[7] 刘星辰, 周奇才. 一维卷积神经网络实时抗噪故障诊断算法[J]. 哈尔滨工业大学学报, 2019, 51(7): 89-95.

    Liu X C, Zhou Q C. Real-time anti-noise fault diagnosis algorithm of one-dimensional convolutional neural network[J]. Journal of Harbin Institute of Technology, 2019, 51(7): 89-95.

[8] 陈勇, 安汪悦, 刘焕淋, 等. 改进经验模态分解算法在光纤布拉格光栅周界入侵行为分类中的应用[J]. 中国激光, 2019, 46(3): 0304003.

    Chen Y, An W Y, Liu H L, et al. Application of improved empirical mode decomposition algorithm in fiber Bragg grating perimeter intrusion behaviors classification[J]. Chinese Journal of Lasers, 2019, 46(3): 0304003.

[9] 陈勇, 陈亚武, 刘志强, 等. 基于光纤布拉格光栅传感的齿轮故障检测方法[J]. 中国激光, 2020, 47(3): 0304007.

    Chen Y, Chen Y W, Liu Z Q, et al. A gear fault detection method based on a fiber Bragg grating sensor[J]. Chinese Journal of Lasers, 2020, 47(3): 0304007.

[10] Nguyen D, Kang M, Kim C H, et al. Highly reliable state monitoring system for induction motors using dominant features in a two-dimension vibration signal[J]. New Review of Hypermedia and Multimedia, 2013, 19(3/4): 248-258.

[11] 赵呈锐, 金晓峰, 倪大成, 等. 基于塑料光纤和棱镜结构的光纤式转速传感器[J]. 重庆邮电大学学报(自然科学版), 2016, 28(3): 383-388.

    Zhao C R, Jin X F, Ni D C, et al. Optical fiber rotational speed sensor based on plastic optical fiber and optical prism[J]. Journal of Chongqing University of Posts and Telecommunications (Nature Science Edition), 2016, 28(3): 383-388.

[12] Liu H L, Wang C J, Chen Y. An improved genetic algorithm for increasing the addressing accuracy of encoding fiber Bragg grating sensor network[J]. Optical Fiber Technology, 2018, 40: 28-35.

[13] Rauber T W, de Assis Boldt F, Varejão F M. Heterogeneous feature models and feature selection applied to bearing fault diagnosis[J]. IEEE Transactions on Industrial Electronics, 2015, 62(1): 637-646.

[14] 郭翠娟, 李德冲, 荣锋, 等. 基于最大相关波形延拓改进的EEMD方法[J]. 重庆邮电大学学报(自然科学版), 2017, 29(6): 768-775.

    Guo C J, Li D C, Rong F, et al. An improved EEMD method based on maximal correlation waveform extension[J]. Journal of Chongqing University of Posts and Telecommunications (Nature Science Edition), 2017, 29(6): 768-775.

[15] Lee D S, Chang C S, Chang H N. Analyses of the clustering coefficient and the Pearson degree correlation coefficient of chung's duplication model[J]. IEEE Transactions on Network Science and Engineering, 2016, 3(3): 117-131.

[16] 张龙, 毛志德, 杨世锡, 等. 基于包络谱带通峭度的改进谱峭度方法及在轴承诊断中的应用[J]. 振动与冲击, 37( 23): 179- 187.

    ZhangL, Mao ZD, Yang SX, et al. Improved spectral kurtosis method based on envelope bandpass kurtosis and its application in bearing diagnosis[J]. Journal of Vibration and Shock, 37( 23): 179- 187.

[17] Sun J H, Xiao Z W, Xie Y X. Automatic multi-fault recognition in TFDS based on convolutional neural network[J]. Neurocomputing, 2017, 222: 127-136.

[18] SabourS, FrosstN, Hinton GE. Dynamic routing between capsules[C]∥31st Annual Conference on Neural Information Processing Systems, December 3-8, 2018, Long Beach, CA, USA. New York: Curran Associates, 2017: 3857- 3867.

[19] Gao R Q, Yang F W, Yang W M, et al. Margin loss: making faces more separable[J]. IEEE Signal Processing Letters, 2018, 25(2): 308-312.

[20] Smith W A, Fan Z Q, Peng Z X, et al. Optimised spectral kurtosis for bearing diagnostics under electromagnetic interference[J]. Mechanical Systems and Signal Processing, 2016, 75: 371-394.

[21] 龚华平, 杨效, 屠于梦, 等. FBG传感器和电阻应变仪的振动监测特性[J]. 红外与激光工程, 2013, 42(3): 810-813.

    Gong H P, Yang X, Tu Y M, et al. Vibration detection characteristics of FBG sensor and resistance strain gauge[J]. Infrared and Laser Engineering, 2013, 42(3): 810-813.

[22] Kingma DP, Ba J. Adam: a method for stochastic optimization[EB/OL]. ( 2017-01-30)[2020-05-13]. https:∥arxiv.org/abs/1412. 6980.

[23] Huang R Y, Liao Y X, Zhang S H, et al. Deep decoupling convolutional neural network for intelligent compound fault diagnosis[J]. IEEE Access, 2019, 7: 1848-1858.

[24] Zhang H, Huang Q, Li F W, et al. A network security situation prediction model based on wavelet neural network with optimized parameters[J]. Digital Communications and Networks, 2016, 3: 139-144.