常勇, 包广清, 程思凯, 等.基于VMD和KFCM的轴承故障诊断方法优化与研究[J].西南大学学报(自然科学版), 2020, 42(10): 146-155.
郑怀亮, 王日新, 杨远涛, 等.数据驱动故障诊断方法泛化性能的经验性分析[J].机械工程学报, 2020, 56(9): 102-117.
李俊, 刘永葆, 余又红.卷积神经网络和峭度在轴承故障诊断中的应用[J].航空动力学报, 2019, 34(11): 2423-2431.
曲建岭, 余路, 袁涛, 等.基于一维卷积神经网络的滚动轴承自适应故障诊断算法[J].仪器仪表学报, 2018, 39(7): 134-143.
GU X, TANG X H, LU J G, et al. Adaptive Fault Diagnosis Method for Rolling Bearings Based on 1-DCNN-LSTM[J]. Machine Tool & Hydraulics, 2020, 48(6): 107-113.
王裕峰.融合多传感器的故障诊断方法研究[D].北京: 北京交通大学, 2019.
李恒, 张氢, 秦仙蓉, 等.基于短时傅里叶变换和卷积神经网络的轴承故障诊断方法[J].振动与冲击, 2018, 37(19): 124-131.
安晶, 艾萍, 徐森, 等.一种基于一维卷积神经网络的旋转机械智能故障诊断方法[J].南京大学学报(自然科学版), 2019, 55(1): 133-142.
李鸿雁, 苏庭波.基于贝叶斯网络和卷积神经网络的手绘草图识别方法[J].西南师范大学学报(自然科学版), 2019, 44(9): 96-102.
HE K M, ZHANG X Y, REN S Q, et al. Deep Residual Learning for Image Recognition[C] // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, US: IEEE, 2016: 770-778.
SZEGEDY C, LIU W, JIA Y Q, et al. Goingdeeper with Convolutions[C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, US: IEEE, 2015: 1-9.
HUANG G, LIU Z, VAN DER MAATEN L, et al. Densely Connected Convolutional Networks[C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, US: IEEE, 2017: 4700-4708.
HINTON G E, SRIVASTAVA N, KRIZHEVSKY A, et al. Improving Neural Networks by Preventing Co-Adaptation of Feature Detectors[EB/OL].[2012-10-20][2020-09-20]. https://arxiv.org/abs/1207.0580.
SRIVASTAVA N, HINTON G, KRIZHEVSKY A, et al. Dropout: A Simple Way to Prevent Neural Networks From Overfitting[J]. The Journal of Machine Learning Research, 2014, 15(1): 1929-1958.
The Case Western Reserve University Bearing Data Center Website. Bearing Data Center Seeded Fault Test Data[EB/OL].[2007-11-27][2020-09-20]. http://www/eecs/cwru/edu/laboratory/bearing/.
荆云建.基于改进型卷积神经网络的动车组滚动轴承故障诊断方法研究[D].北京: 北京交通大学, 2019.