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A Multistage Deep Transfer Learning Method for Machinery Fault Diagnostics Across Diverse Working Conditions and Devices

机译：多级深度转移学习方法，用于各种工作条件和设备的机械故障诊断

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Jian Zhou; Lian-Yu Zheng; Yiwei Wang; Christian Gogu;
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1. A New Deep Transfer Learning Method for Bearing Fault Diagnosis Under Different Working Conditions [J] . Zhu Jun, Chen Nan, Shen Changqing IEEE sensors journal . 2020,第15期

机译：不同工作条件下轴承故障诊断的新型深度转移学习方法
2. Deep multi-scale convolutional transfer learning network: A novel method for intelligent fault diagnosis of rolling bearings under variable working conditions and domains [J] . Zhao Bo, Zhang Xianmin, Zhan Zhenhui, Neurocomputing . 2020,第Sepa24期

机译：深度多尺度卷积转移学习网络：可变工作条件与域下滚动轴承智能故障诊断的新方法
3. A deformable CNN-DLSTM based transfer learning method for fault diagnosis of rolling bearing under multiple working conditions [J] . Wang Zheng, Liu Qingxiu, Chen Hansi, International Journal of Production Research . 2021,第15a16期

机译：基于可变形的CNN-DLSTM转移学习方法，用于多个工作条件下滚动轴承的故障诊断
4. A New Deep Transfer Learning Network for Fault Diagnosis of Rotating Machine Under Variable Working Conditions [C] . Weiwei Qian, Shunming Li, Jinrui Wang, Prognostics and System Health Management Conference . 2018

机译：变工况下旋转机械故障诊断的新型深度转移学习网络
5. Development of methodologies for evaluating performance of fault detection and diagnostics protocols applied to unitary air-conditioning equipment. [D] . Yuill, David P. 2014

机译：开发用于评估应用于一体式空调设备的故障检测和诊断协议性能的方法。
6. A Novel Deep Learning Method for Intelligent Fault Diagnosis of Rotating Machinery Based on Improved CNN-SVM and Multichannel Data Fusion [O] . Wenfeng Gong, Hui Chen, Zehui Zhang, 2019

机译：基于改进的CNN-SVM和多通道数据融合的旋转机械智能故障诊断深度学习新方法
7. A Novel Rotating Machinery Fault Diagnosis Method Based on Adaptive Deep Belief Network Structure and Dynamic Learning Rate Under Variable Working Conditions [O] . Peiming Shi, Peng Xue, Aoyun Liu, 2021

机译：基于自适应深度信仰网络结构的新型旋转机械故障诊断方法和可变工作条件下的动态学习率

A Multistage Deep Transfer Learning Method for Machinery Fault Diagnostics Across Diverse Working Conditions and Devices

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