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Mixed Kernel Functions for Multivariate Statistical Monitoring of Nonlinear Processes

Pilario, Karl Ezra S. and Shafiee, Mahmood (2020) Mixed Kernel Functions for Multivariate Statistical Monitoring of Nonlinear Processes. In: Advances in Asset Management and Condition Monitoring: COMADEM 2019. Smart Innovation, Systems and Technologies . Springer, Cham, Switzerland, pp. 61-67. ISBN 978-3-030-57744-5. E-ISBN 978-3-030-57745-2. (doi:10.1007/978-3-030-57745-2) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:87430)

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Machine learning techniques have now become pervasive in the field of process condition monitoring. In particular, kernel methods are those that use kernel functions to allow for the efficient nonlinear analysis of process data by projecting them onto high-dimensional spaces. A widely used kernel machine in multivariate process monitoring is kernel principal components analysis (KPCA). Many choices of kernel functions were used in previous KPCA studies. However, the use of single kernels alone was recently shown to give only limited expressive ability. In this work, we explored the impact of combining various kernel functions to the performance of KPCA for condition monitoring. Fault detection performance is defined by percent correct detection of faulty states and non-detection of normal states. Optimal kernel parameters were obtained using the genetic algorithm (GA). Visualizations of the boundary between normal and faulty states are provided for demonstration in a chemical process case study. This work can inform the development of mixed kernels for nonlinear process monitoring, not only in KPCA, but also in other kernel machines.

Item Type: Book section
DOI/Identification number: 10.1007/978-3-030-57745-2
Uncontrolled keywords: Kernel PCA, Nonlinear process, Multivariate analysis, Mixed kernel, Fault detection
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA165 Engineering instruments, meters etc. Industrial instrumentation
T Technology > TA Engineering (General). Civil engineering (General) > TA401 Materials engineering and construction
T Technology > TJ Mechanical engineering and machinery
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Mahmood Shafiee
Date Deposited: 02 Apr 2021 20:03 UTC
Last Modified: 07 Apr 2021 15:10 UTC
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Pilario, Karl Ezra S.:
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