Knowledge Transfer for Rotary Machine Fault Diagnosis

Yan Ruqiang; Shen Fei; Sun Chuang; Chen Xuefeng

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Knowledge Transfer for Rotary Machine Fault Diagnosis

机译：旋转机器故障诊断的知识转移

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This paper intends to provide an overview on recent development of knowledge transfer for rotary machine fault diagnosis (RMFD) by using different transfer learning techniques. After brief introduction of parameter-based, instance-based, feature-based and relevance-based knowledge transfer, the applications of knowledge transfer in RMFD are summarized from four categories: transfer between multiple working conditions, transfer between multiple locations, transfer between multiple machines, and transfer between multiple fault types. Case studies on four datasets including gears, bearing, and motor faults verified effectiveness of knowledge transfer on improving diagnostic accuracy. Meanwhile, research trends on transfer learning in the field of RMFD are discussed.

机译：本文旨在通过使用不同的转移学习技术来概述旋转机器故障诊断（RMFD）的知识转移的概要。在简要介绍基于参数的基于事实的基于，特征和基于相关的知识转移之后，RMFD中知识传输的应用总结了四类：在多个工作条件之间传输，在多个位置之间传输，在多台机器之间传输，并在多个故障类型之间传输。案例研究四个数据集包括齿轮，轴承和电机故障验证了知识转移的有效性，以提高诊断准确性。同时，讨论了RMFD领域转移学习的研究趋势。

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来源
《IEEE sensors journal》 |2020年第15期|8374-8393|共20页
作者
Yan Ruqiang; Shen Fei; Sun Chuang; Chen Xuefeng;
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作者单位

Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China;

Southeast Univ Sch Instrument Sci & Engn Nanjing 210096 Peoples R China;

Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China;

Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China;

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正文语种 eng
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关键词
Knowledge transfer; Support vector machines; Fault diagnosis; Adaptation models; Sensors; Market research; Rotors; Transfer learning; rotary machine fault diagnosis; multiple working conditions; multiple locations; multiple machines; multiple fault types;

机译：知识转移;支持向量机;故障诊断;适应模型;传感器;市场研究;转子;转移学习;旋转机器故障诊断;多个工作条件;多个机器;多个故障类型;

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专利

1. Multi-source transfer learning network to complement knowledge for intelligent diagnosis of machines with unseen faults [J] . Bin Yang, Songci Xu, Yaguo Lei, Mechanical systems and signal processing . 2022,第Jana期

机译：多源转移学习网络，以便与看不见的故障智能诊断的知识
2. Intelligent Fault Diagnosis for Rotary Machinery Using Transferable Convolutional Neural Network [J] . Chen Zhuyun, Gryllias Konstantinos, Li Weihua IEEE transactions on industrial informatics . 2020,第1期

机译：可转移卷积神经网络旋转机械智能故障诊断
3. Intelligent Fault Diagnosis Based on Receptive Field of DCNN for Rotary Machine under Variable Conditions [J] . Weigang Wen, Yihao Bai, Fangquan Hu, Procedia Manufacturing . 2020,第186期

机译：基于可变条件下旋转机器的DCNN接收领域的智能故障诊断
4. A knowledge base system for rotary equipment fault detection and diagnosis [C] . The 11th International Conference on Control Automation Robotics and Vision . 2010

机译：旋转设备故障检测与诊断的知识库系统
5. A fault diagnosis system for rotary machinery supported by rolling element bearings. [D] . Hasanzadeh Ghafari, Shahab. 2007

机译：滚动轴承支撑的旋转机械故障诊断系统。
6. Fault Diagnosis of Rotary Machines Using Deep Convolutional Neural Network with Wide Three Axis Vibration Signal Input [O] . Davor Kolar, Dragutin Lisjak, Michał Pająk, 2020

机译：具有宽三轴振动信号输入的深卷积神经网络的旋转机器故障诊断
7. Intelligent Fault Diagnosis for Rotary Machinery Using Transferable Convolutional Neural Network [O] . Zhuyun Chen, Konstantinos Gryllias, Weihua Li 2020

机译：可转移卷积神经网络旋转机械智能故障诊断

Knowledge Transfer for Rotary Machine Fault Diagnosis

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