schwxd / Multi-Task-Learning-Papers

A collection of multi-task-learning / transfer-learning papers related to fault diagnosis

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Multi-Task-Learning-Papers

2018

  • Deep Asymmetric Multi-task Feature Learning Lee H, Yang E, Hwang S J. arXiv. [pdf]

  • Zhang, Ansi, et al. Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation. Applied Sciences 8.12 (2018): 2416. [pdf]

    使用Bi-LSTM加Fine-Tuning的方式,在CMAPSS的不同工况数据之间进行剩余寿命预测。

2017

  • 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, PP(99):1-1. [pdf]

  • Wen, Long, Liang Gao, and Xinyu Li. A New deep transfer learning based on sparse auto-encoder for fault diagnosis. IEEE Transactions on Systems, Man, and Cybernetics: Systems (2017). [pdf]

    在西储大学振动数据不同工况数据之间进行迁移学习。通过三层稀疏自编码网络,分别生成源和目标数据的特征向量,优化函数包含两部分:源数据的分类Loss、两个特征向量的最大均值差异MMD

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A collection of multi-task-learning / transfer-learning papers related to fault diagnosis