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Methodology Research on Paper Breaking Fault Diagnosis Based on Transfer Learning
Received:June 08, 2024  
DOI：10.11980/j.issn.0254-508X.2024.12.020
Key Words:transfer learning  paper breaking  fault diagnosis  methodology research  working condition
Fund Project:山西省教育科学“十四五”规划2021年度课题（GH-21238）；山西省重点研发计划（202102100401004）；山西重点国际科技合作项目（202104041101005）；广州市基础与应用基础研究（2023A04J1367）。
Author NameAffiliationPostcode
SONG Limin Shanxi Institute of Mechanical ＆Electrical Engineering， Changzhi， Shanxi Province， 046011 046011
GAO Guijun* College of Mechanical Engineering and Vehicle， Taiyuan University of Technology， Taiyuan， Shanxi Province， 030024 030024
HE Zhenglei State Key Lab of Pulp and Paper Engineering， South China University of Technology， Guangzhou， Guangdong Province， 510640 510640
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Abstract:Aiming at the shortage of paper breaking fault marker data and the difficulty of reusing fault diagnosis modeling due to the frequent switching of production conditions， this paper proposed a modeling method of paper breaking fault migration model based on parameters and features， respectively. By analyzing the data distribution characteristics of quantitative setpoints and their strongly correlated variables， the basic working conditions of industrial data were divided. The reliability of the working condition division was verified by the evaluation of Mahalanobis distance and multi-core maximum mean difference equidistance function. Based on the divided working condition data， the paper breaking fault model established according to the working condition with more effective paper breaking fault data was transferred to the working condition with missing data. The results showed that the established fault diagnosis transfer model could achieve 98.3%， 94.6%， and 96.4% diagnostic accuracy in different working conditions， respectively， which improved the universality of the model and promoted the wider and more accurate fault diagnosis for different papermaking processes.
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