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Data-driven fault detection methods for detecting small-magnitude faults in anaerobic digestion process

Abstract : Early detection of small-magnitude faults in anaerobic digestion (AD) processes is a mandatory step for preventing serious consequence in the future. Since volatile fatty acids (VFA) accumulation is widely suggested as a process health indicator, a VFA soft-sensor was developed based on support vector machine (SVM) and used for generating the residuals by comparing real and predicted VFA. The estimated residual signal was applied to univariate statistical control charts such as cumulative sum (CUSUM) and square prediction error (SPE) to detect the faults. A principal component analysis (PCA) model was also developed for comparison with the aforementioned approach. The proposed framework showed excellent performance for detecting small-magnitude faults in the state parameters of AD processes.
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https://hal.inrae.fr/hal-03000901
Contributor : Dominique Fournier <>
Submitted on : Thursday, November 12, 2020 - 10:31:26 AM
Last modification on : Friday, February 5, 2021 - 4:04:44 AM

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Pezhman Kazemi, Jaume Giralt, Christophe Bengoa, Jean-Philippe Steyer. Data-driven fault detection methods for detecting small-magnitude faults in anaerobic digestion process. Water Science and Technology, IWA Publishing, 2020, 81 (8), pp.1740-1748. ⟨10.2166/wst.2020.026⟩. ⟨hal-03000901⟩

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