人工智能文献记录专栏，专栏地址：https://blog.csdn.net/u014157632/category_9760481.html，总目录：https://blog.csdn.net/u014157632/article/details/104578738。不定期更新

2020年

• Li X, Hu Y, Li M, et al. Fault diagnostics between different type of components: A transfer learning approach[J]. Applied Soft Computing, 2020, 86: 105950.
  fine-tune方法

2019年

• Sun M, Wang H, Liu P, et al. A sparse stacked denoising autoencoder with optimized transfer learning applied to the fault diagnosis of rolling bearings[J]. Measurement, 2019, 146: 305-314.
  基于自编码器的域适配
• Yang B, Lei Y, Jia F, et al. An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings[J]. Mechanical Systems and Signal Processing, 2019, 122: 692-706.
  用MMD进行多层域适配，CNN
• Hasan M J, Islam M M M, Kim J M. Acoustic spectral imaging and transfer learning for reliable bearing fault diagnosis under variable speed conditions[J]. Measurement, 2019, 138: 620-631.
  fine-tune方法
• Zhao Z, Zhang Q, Yu X, et al. Unsupervised Deep Transfer Learning for Intelligent Fault Diagnosis: An Open Source and Comparative Study[J]. arXiv preprint arXiv:1912.12528, 2019.
  用现有的域适配方法在5个数据集上做了benchmark
• Qian W, Li S, Jiang X. Deep transfer network for rotating machine fault analysis[J]. Pattern Recognition, 2019, 96: 106993.
  用自编码器，用auto-balanced high-order Kullback-Leibler、smooth conditional distribution alignment、weighted joint distribution
  alignment域做适配
• Han T, Liu C, Yang W, et al. Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application[J]. ISA transactions, 2019.
  用JDA作域适配
• Wang Q, Michau G, Fink O. Domain adaptive transfer learning for fault diagnosis[C]//2019 Prognostics and System Health Management Conference (PHM-Paris). IEEE, 2019: 279-285.
  用DANN作域适配
• Li X, Zhang W, Ding Q, et al. Multi-layer domain adaptation method for rolling bearing fault diagnosis[J]. Signal Processing, 2019, 157: 180-197.
  CNN、MMD域适配
• Cheng C, Zhou B, Ma G, et al. Wasserstein distance based deep adversarial transfer learning for intelligent fault diagnosis[J]. arXiv preprint arXiv:1903.06753, 2019.
• Zhang M, Wang D, Lu W, et al. A deep transfer model with wasserstein distance guided multi-adversarial networks for bearing fault diagnosis under different working conditions[J]. IEEE Access, 2019, 7: 65303-65318.

2018年

• Li X, Zhang W, Ding Q. A robust intelligent fault diagnosis method for rolling element bearings based on deep distance metric learning[J]. Neurocomputing, 2018, 310: 77-95.
• Guo L, Lei Y, Xing S, 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, 2018, 66(9): 7316-7325.
  结合DANN和MMD的域适配
• Shao S, McAleer S, Yan R, et al. Highly accurate machine fault diagnosis using deep transfer learning[J]. IEEE Transactions on Industrial Informatics, 2018, 15(4): 2446-2455.
  频谱图、CNN、fine-tune
• Cao P, Zhang S, Tang J. Preprocessing-free gear fault diagnosis using small datasets with deep convolutional neural network-based transfer learning[J]. Ieee Access, 2018, 6: 26241-26253.
  fine-tune方法

2017年

• Wen L, Gao L, Li X. A new deep transfer learning based on sparse auto-encoder for fault diagnosis[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017, 49(1): 136-144.
  使用MMD，基于自编码器
• Zhang W, Peng G, Li C, et al. A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals[J]. Sensors, 2017, 17(2): 425.
  使用AdaBN的域适配，第一层使用大卷积核
• Zhang B, Li W, Tong Z, et al. Bearing fault diagnosis under varying working condition based on domain adaptation[J]. arXiv preprint arXiv:1707.09890, 2017.
  传统方法域适配，subspace alignment (SA)
• Zhang R, Tao H, Wu L, et al. Transfer learning with neural networks for bearing fault diagnosis in changing working conditions[J]. IEEE Access, 2017, 5: 14347-14357.
  fine-tune方法

2016年

• Wang J, Xie J, Zhang L, et al. A factor analysis based transfer learning method for gearbox diagnosis under various operating conditions[C]//2016 International Symposium on Flexible Automation (ISFA). IEEE, 2016: 81-86.
  基于传统方法的迁移学习
• Lu W, Liang B, Cheng Y, et al. Deep model based domain adaptation for fault diagnosis[J]. IEEE Transactions on Industrial Electronics, 2016, 64(3): 2296-2305.
  用MMD、自编码器提取特征，SVM分类

2015年

• Shen F, Chen C, Yan R, et al. Bearing fault diagnosis based on SVD feature extraction and transfer learning classification[C]//2015 Prognostics and System Health Management Conference (PHM). IEEE, 2015: 1-6.
  SVD提取特征，传统迁移学习

最后

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