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Second, by weighting the data of different importance, the role of data similar to test samples in the modeling process is strengthened. Third, the LWPKPCA model is applied to process monitoring, the monitoring indicators are established in a high-dimensional space and used to detect faults. Finally, on the basis of LWPKPCA, the penicillin fermentation process (PFP) is taken to evaluate the monitoring performance of the proposed methods. According to the comparison of the experiment results, the detection rate and accuracy rate of the LWPKPCA method is considerably better than those of probabilistic principal component analysis and probabilistic kernel principal component analysis methods. The results demonstrate that the proposed method is suitable for processing time-varying data with nonlinear characteristics, and the LWPKPCA process monitoring method is effective for improving the performance of fault detection.<\/jats:p>","DOI":"10.3233\/jifs-224383","type":"journal-article","created":{"date-parts":[[2023,6,2]],"date-time":"2023-06-02T13:24:36Z","timestamp":1685712276000},"page":"5795-5805","source":"Crossref","is-referenced-by-count":2,"title":["A new monitoring approach of time-varying and nonlinear processes with application to penicillin fermentation process"],"prefix":"10.1177","volume":"45","author":[{"given":"Ying","family":"Xie","sequence":"first","affiliation":[{"name":"College of Information Engineering, Shenyang University of Chemical Technology, Shenyang, China"},{"name":"Liaoning Key Laboratory of Industry-Environment-Resource Collaborative Control and Optimization Technology, Shenyang University of Chemical Technology, Shenyang, 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