[1] SAWALHI N, RANDALL R B. Gear parameter identification in a wind turbine gearbox using vibration signals[J]. Mechanical Systems & Signal Processing, 2014, 42(1/2):368-376. [2] 杨大为, 赵永东, 冯辅周, 等.基于参数优化变分模态分解和多尺度熵偏均值的行星变速箱故障特征提取[J].兵工学报,2018,39(9):1683-1691. YANG D W, ZHAO Y D, FENG F Z, et al. Planetary gearbox fault feature extraction based on parameter optimized variational mode decomposition and partial mean of multi-scale entropy[J]. Acta Armamentarii, 2018,39(9):1683-1691. (in Chinese) [3] 丁闯, 张兵志, 冯辅周, 等. 局部均值分解和排列熵在行星齿轮箱故障诊断中的应用[J]. 振动与冲击, 2017, 36(17):55-60. DING C, ZHANG B Z, FENG F Z, et al. Application of local mean decomposition and permutation entropy in fault diagnosis of planetary gearboxes[J]. Journal of Vibration and Shock, 2017, 36(17):55-60. (in Chinese) [4] DOUZAS G, BACAO F. Effective data generation for imbalanced learning using conditional generative adversarial networks[J]. Expert Systems with Applications, 2018, 91:464-471. [5] 杨宇, 潘海洋, 李永国, 等. 一种增量式半监督VPMCD齿轮故障在线诊断方法[J]. 振动与冲击, 2015,34(8):49-54. YANG Y, PAN H Y, LI Y G, et al. A novel incremental semi-supervised VPMCD gear fault on-line diagnosis method[J]. Journal of Vibration and Shock, 2015,34(8):49-54. (in Chinese) [6] YANG Q, CHEN G M, HE QF, et al. The research of pattern recognition of gear pump based on EMD and KPCA-SVM[C]∥Proceedings of International Conference on System Science, Engineering Design and Manufacturing Informatization. Guiyang, China: International Conference on System Science, 2011:1104-1109. [7] GOLAFSHAN R, SANLITURK K Y. SVD and Hankel matrix based de-noising approach for ball bearing fault detection and its assessment using artificial faults[J]. Mechanical Systems & Signal Processing, 2016, 70:36-50. [8] 张文斌, 苏艳萍, 普亚松, 等. 基于集合经验模式分解能量分布与灰色相似关联度的齿轮故障诊断[J]. 机械工程学报, 2014, 50(7):70-77. ZHANG W B, SU Y P, PU Y S, et al. Gear fault diagnosis method using ensemble empirical mode decomposition energy distribution and grey similar incidence[J]. Journal of Mechanical Engineering, 2014, 50(7):70-77. (in Chinese) [9] GOODFELLOW I J, POUGETABADIE J, MIRZA M, et al. Generative adversarial networks[J]. Advances in Neural Information Processing Systems, 2014, 3:2672-2680.
[10] JING L Y, ZHAO M, LI P, et al. A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox[J]. Measurement, 2017, 111:1-10. [11] INCE T, KIRANYAZ S, EREN L, et al. Real-time motor fault detection by 1-D convolutional neural networks[J]. IEEE Tran- sactions on Industrial Electronics, 2016, 63(11):7067-7075. [12] CHAWLA N V, BOWYER K W, HALL L O, et al. SMOTE: synthetic minority over-sampling technique[J]. Journal of Artificial Intelligence Research, 2002, 16(1) : 321-357. [13] BUNKHUMPORNPAT C, SINAPIROMSARAN K, LURSINSAP C. Safe-level-SMOTE: safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem[C]∥Proceedings of Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining. Hyderabad, India: Springer-Verilog, 2009:475-482. [14] HAN H, WANG W Y, MAO B H. Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning[C]∥Advances in Intelligent Computing, International Conference on Intelligent Computing. Berlin, Germany: Springer, 2005:878-887. [15] CIESLAK D A, CHAWLA N V, STRIEGEL A. Combating imbalance in network intrusion datasets[C]∥Proceedings of International Conference on Granular Computing. Atlanta, GA, US: IEEE, 2006:732-737.
第40卷 第7期2019 年7月兵工学报ACTA ARMAMENTARIIVol.40No.7Jul.2019
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