Advanced data-driven fault detection in gas-to-liquid plants
<p dir="ltr">Fault detection is a critical part of process monitoring, where the objective is to flag unexpected operating behavior quickly and accurately. In this paper, a novel extension of the Generalized Likelihood Ratio charts is proposed, denoted as the Maximum Multivariate GLR...
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2025
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| Summary: | <p dir="ltr">Fault detection is a critical part of process monitoring, where the objective is to flag unexpected operating behavior quickly and accurately. In this paper, a novel extension of the Generalized Likelihood Ratio charts is proposed, denoted as the Maximum Multivariate GLR charts. Linear and nonlinear data-driven models, namely principal component analysis and its kernel extension and <u>neural networks</u>, are combined with different statistical charts towards the detection of multiple fault types in three distinct case studies: synthetic, Tennessee Eastman process, and Gas-to-Liquid process. The results show that the MMGLR charts have a better detection accuracy than conventional charts, and that <u>neural networks</u> are more robust modeling techniques than PCA and KPCA for the sake of fault detection.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: Computers & Chemical Engineering<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.compchemeng.2025.109098" target="_blank">https://dx.doi.org/10.1016/j.compchemeng.2025.109098</a></p> |
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