Affiliations: [a]
College of Information Engineering, Shenyang University of Chemical Technology, Shenyang, China
| [b]
Liaoning Key Laboratory of Industry-Environment-Resource Collaborative Control and Optimization Technology, Shenyang University of Chemical Technology, Shenyang, China
| [c]
College of Light Industry, Liaoning University, Shenyang, China
Correspondence:
[*]
Corresponding author. Lirong Zhai, College of Light Industry, Liaoning University, Shenyang, China. E-mail: zhailirong@126.com.
Abstract: In the actual production process, time-varying and nonlinear problems are numerous important problems to be considered, in view of these problems, a process monitoring approach based on locally weighted probabilistic kernel principal component analysis (LWPKPCA) is proposed. First, the method selects the normal process data with a high similarity to the test samples as training data of the local model, and continuously updates the local model according to the test samples to build an accurate time-varying model. 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.
Keywords: Locally weighted probabilistic kernel principal component analysis, process monitoring, fault detection
