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Mixed Kernel Canonical Variate Dissimilarity Analysis for Incipient Fault Monitoring in Nonlinear Dynamic Processes

Pilario, Karl Ezra S., Cao, Yi, Shafiee, Mahmood (2019) Mixed Kernel Canonical Variate Dissimilarity Analysis for Incipient Fault Monitoring in Nonlinear Dynamic Processes. Computers & Chemical Engineering, 123 . pp. 143-154. ISSN 0098-1354. (doi:10.1016/j.compchemeng.2018.12.027) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:79735)

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Incipient fault monitoring is becoming very important in large industrial plants, as the early detection of incipient faults can help avoid major plant failures. Recently, Canonical Variate Dissimilarity Analysis (CVDA) has been shown to be an efficient technique for incipient fault detection, especially under dynamic process conditions. CVDA can be extended to nonlinear processes by introducing kernel-based learning. Incipient fault monitoring requires kernels with both good interpolation and extrapolation abilities. However, conventional single kernels only exhibit one ability or the other, but not both. To overcome this drawback, this study presents a Mixed Kernel CVDA method for incipient fault monitoring in nonlinear dynamic processes. Due to the use of mixed kernels, both enhanced detection sensitivity and a better depiction of the growing fault severity in the monitoring charts are achieved. Looking ahead, this work takes a step towards understanding the impact of kernel behavior in process monitoring performance.

Item Type: Article
DOI/Identification number: 10.1016/j.compchemeng.2018.12.027
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA165 Engineering instruments, meters etc. Industrial instrumentation
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: 23 Jan 2020 18:13 UTC
Last Modified: 16 Feb 2021 14:11 UTC
Resource URI: (The current URI for this page, for reference purposes)
Pilario, Karl Ezra S.:
Cao, Yi:
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