Full-Text Search:
Home|Journal Papers|About CNKI|User Service|FAQ|Contact Us|中文
《Aeronautical Science & Technology》 2020-01
Add to Favorite Get Latest Update

On-line Fault Diagnosis of Rolling Bearing Based on Transfer Learning

Mao Guantong;Hong Liu;Wang Jinglin;Mechanical and Electronic Engineering,Wuhan University of Technology;Aviation Key Laboratory of Science and Technology on Fault Diagnosis and Health Management,Shanghai Aero Measurement & Control Technology Research Institute;  
Fault detection and diagnosis is the key to the reliable and safe operation of industrial equipment. In recent years, deep learning algorithms have been widely used in data-driven fault detection because of its capability of automatic feature recognition. Generally, deep learning algorithms are trained by historical data for the same problem and then tested in trained models using homologous new data. As the transfer learning has the potential to address the problems that are different but still similar with each other in target domain and source domain, this paper proposes an online fault diagnosis method based on a deep Transfer Convolutional Neural Network(TCNN)framework. First, the time-domain signal data is transformed into a time-frequency domain containing rich information by Short Time Fourier Transform(STFT), which serves as the input that is suitable for Convolutional Neural Network(CNN).Then, an online CNN network is constructed, which can automatically extract features from frequency-domain images and classify faults. Finally, in order to improve the real-time performance of online CNN, several offline CNN are constructed and the relevant data sets are pre-trained. By transferring the shallow structure of offline CNN to online CNN, online CNN can significantly improve the real-time performance and successfully solve the problem of achieving the expected diagnostic accuracy within the limited training time. The proposed method is verified on the bearing fault data set of CWRU Bearing Data Center, and achieve the expected diagnostic accuracy.
【Fund】: 航空科学基金(20173365001)~~
【CateGory Index】: TH133.33;TP18;TP391.41
Download(CAJ format) Download(PDF format)
CAJViewer7.0 supports all the CNKI file formats; AdobeReader only supports the PDF format.
©2006 Tsinghua Tongfang Knowledge Network Technology Co., Ltd.(Beijing)(TTKN) All rights reserved