Abstract
Increasing complexity of industrial processes has made statistical methods for process monitoring and diagnosis a more attractive alternative to model-based methods. A primary reason is that statistical approaches can be formulated to rely less on process knowledge. Since multivariable processes can exhibit complex, nonlinear dynamics, there is a need for methods capable of diagnosing nonlinear process data. A Monte Carlo simulation was conducted on a numerical model of the quadruple tank process (QTP) - a novel multivariate nonlinear process. The simulation was designed so that the QTP exhibited bipartite nonlinear behavior. Reference data obtained from the simulation was used to obtain principal component analysis (PCA) and autoencoder (AE) models. The models generated residuals that were used to monitor the condition of the process. The results showed that AEs, which have nonlinear functionalities, performed better than PCA models at generating residuals.
Original language | English |
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Title of host publication | Proceedings of the 3rd IEEE Conference on Control Technology and Applications |
Publisher | IEEE |
Publication date | Aug 2019 |
Pages | 994-999 |
Article number | 8920588 |
ISBN (Electronic) | 9781728127675 |
DOIs | |
Publication status | Published - Aug 2019 |
Event | 2019 IEEE Conference on Control Technology and Applications - City University of Hong Kong, Hong Kong, China Duration: 19 Aug 2019 → 21 Aug 2019 Conference number: 3 https://ccta2019.ieeecss.org/ |
Conference
Conference | 2019 IEEE Conference on Control Technology and Applications |
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Number | 3 |
Location | City University of Hong Kong |
Country/Territory | China |
City | Hong Kong |
Period | 19/08/2019 → 21/08/2019 |
Sponsor | City University of Hong Kong, Hong Kong Automatic Control Association, IEEE |
Internet address |