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Article

Multimode Process Monitoring Based on Modified Density Peak Clustering and Parallel Variational Autoencoder

1
College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
2
State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China
*
Author to whom correspondence should be addressed.
Mathematics 2022, 10(14), 2526; https://doi.org/10.3390/math10142526
Submission received: 12 June 2022 / Revised: 19 July 2022 / Accepted: 19 July 2022 / Published: 20 July 2022
(This article belongs to the Special Issue Engineering Calculation and Data Modeling)

Abstract

Clustering algorithms and deep learning methods have been widely applied in the multimode process monitoring. However, for the process data with unknown mode, traditional clustering methods can hardly identify the number of modes automatically. Further, deep learning methods can learn effective features from nonlinear process data, while the extracted features cannot follow the Gaussian distribution, which may lead to incorrect control limit for fault detection. In this paper, a comprehensive monitoring method based on modified density peak clustering and parallel variational autoencoder (MDPC-PVAE) is proposed for multimode processes. Firstly, a novel clustering algorithm, named MDPC, is presented for the mode identification and division. MDPC can identify the number of modes without prior knowledge of mode information and divide the whole process data into multiple modes. Then, the PVAE is established based on distinguished multimode data to generate the deep nonlinear features, in which the generated features in each VAE follow the Gaussian distribution. Finally, the Gaussian feature representations obtained by PVAE are provided to construct the statistics H2, and the control limits are determined by the kernel density estimation (KDE) method. The effectiveness of the proposed method is evaluated by the Tennessee Eastman process and semiconductor etching process.
Keywords: multimode process; density peak clustering; variational autoencoder; kernel density estimation; Tennessee Eastman process multimode process; density peak clustering; variational autoencoder; kernel density estimation; Tennessee Eastman process

Share and Cite

MDPI and ACS Style

Yu, F.; Liu, J.; Liu, D. Multimode Process Monitoring Based on Modified Density Peak Clustering and Parallel Variational Autoencoder. Mathematics 2022, 10, 2526. https://doi.org/10.3390/math10142526

AMA Style

Yu F, Liu J, Liu D. Multimode Process Monitoring Based on Modified Density Peak Clustering and Parallel Variational Autoencoder. Mathematics. 2022; 10(14):2526. https://doi.org/10.3390/math10142526

Chicago/Turabian Style

Yu, Feng, Jianchang Liu, and Dongming Liu. 2022. "Multimode Process Monitoring Based on Modified Density Peak Clustering and Parallel Variational Autoencoder" Mathematics 10, no. 14: 2526. https://doi.org/10.3390/math10142526

APA Style

Yu, F., Liu, J., & Liu, D. (2022). Multimode Process Monitoring Based on Modified Density Peak Clustering and Parallel Variational Autoencoder. Mathematics, 10(14), 2526. https://doi.org/10.3390/math10142526

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