[1] 丁恩杰, 俞啸, 夏冰等.矿山信息化发展及以数字孪生为核心的智慧矿山关键技术[J/OL].煤炭学报:1-18[2022-02-13].DOI:10.13225/j.cnki.jccs.YG21. 1930.
[2]丁恩杰, 俞啸, 廖玉波等.基于物联网的矿山机械设备状态智能感知与诊断[J].煤炭学报, 2020, 45(06):2308-2319
[3]张梅, 许桃, 孙辉煌, 孟祥宇.基于模糊故障树和贝叶斯网络的矿井提升机故障诊断[J].工矿自动化, 2020, 46(11):1-5
[4]马辉, 车迪, 牛强, 夏士雄.基于深度神经网络的提升机轴承故障诊断研究[J].计算机工程与应用, 2019, 55(16):123-129
[5] Wei D, Han T, Chu F, et al.Weighted domain adaptation networks for machinery fault diagnosis[J]. Mechanical Systems and Signal Processing, 2021, 158: 107744.
[6]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(07):1-8
[7]张西宁, 余迪, 刘书语.基于迁移学习的小样本轴承故障诊断方法研究[J].西安交通大学学报, 2021, 55(10):30-37
[8] Zhiyi H, Haidong S, Lin J, et al.Transfer fault diagnosis of bearing installed in different machines using enhanced deep auto-encoder[J]. Measurement, 2020, 152: 107393.
[9] 康守强, 刘旺辉, 王玉静等.基于深度在线迁移的变负载下滚动轴承故障诊断方法[J/OL].控制与决策:1-10[2021-08-13].https://doi.org/10.13195/j.kzyjc.2020. 1686.
[10]袁亮, 俞啸, 丁恩杰等.矿山物联网人-机-环状态感知关键技术研究[J].通信学报, 2020, 41(02):1-12
[11]刘飞, 陈仁文, 邢凯玲等.基于迁移学习与深度残差网络的滚动轴承快速故障诊断算法[J].振动与冲击, 2022, 41(03):154-164
[12] He K, Zhang X, Ren S, et al.Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778.
[13]Pan S J, Tsang I W, Kwok J T, et al.Domain Adaptation via Transfer Component Analysis[J].IEEE Transactions on Neural Networks, 2011, 22(2):199-210
[14] Long M, Wang J, Ding G, et al.Transfer Feature Learning with Joint Distribution Adaptation[C]// Proceedings of the 2013 IEEE International Conference on Computer Vision. IEEE, 2013.
[15] 董飞, 俞啸, 丁恩杰等.一种基于小波包变换和监督NPE的滚动轴承故障诊断方法[J].机械设计与制造, 2020(03):29-33.
[16] 俞啸.数据驱动的滚动轴承故障特征分析与诊断方法研究[D].中国矿业大学, 2017.
[17] Fei D, Xiao Y, Enjie D, et al.Rolling Bearing Fault Diagnosis Using Modified Neighborhood Preserving Embedding and Maximal Overlap Discrete Wavelet Packet Transform with Sensitive Features Selection[J]. Shock and Vibration, 2018, 2018:1-29. |