Methodology Research on Paper Breaking Fault Diagnosis Based on Transfer Learning
Aiming at the shortage of paper breaking fault marker data and the difficulty of reusing fault diagnosis modeling due to the fre-quent switching of production conditions,this paper proposed a modeling method of paper breaking fault migration model based on parame-ters and features,respectively.By analyzing the data distribution characteristics of quantitative setpoints and their strongly correlated vari-ables,the basic working conditions of industrial data were divided.The reliability of the working condition division was verified by the evalu-ation 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 pro-moted the wider and more accurate fault diagnosis for different papermaking processes.
transfer learningpaper breakingfault diagnosismethodology researchworking condition
万方数据
维普
