Improved Fault Detection in Chemical Engineering Processes via Non-Parametric Kolmogorov–Smirnov-Based Monitoring Strategy
Fault detection is crucial in maintaining reliability, safety, and consistent product quality in chemical engineering processes. Accurate fault detection allows for identifying anomalies, signaling deviations from the system’s nominal behavior, ensuring the system operates within desired performance...
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MDPI AG
2023-12-01
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| Series: | ChemEngineering |
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| Online Access: | https://www.mdpi.com/2305-7084/8/1/1 |
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| author | K. Ramakrishna Kini Muddu Madakyaru Fouzi Harrou Mukund Kumar Menon Ying Sun |
| author_facet | K. Ramakrishna Kini Muddu Madakyaru Fouzi Harrou Mukund Kumar Menon Ying Sun |
| author_sort | K. Ramakrishna Kini |
| collection | DOAJ |
| description | Fault detection is crucial in maintaining reliability, safety, and consistent product quality in chemical engineering processes. Accurate fault detection allows for identifying anomalies, signaling deviations from the system’s nominal behavior, ensuring the system operates within desired performance parameters, and minimizing potential losses. This paper presents a novel semi-supervised data-based monitoring technique for fault detection in multivariate processes. To this end, the proposed approach merges the capabilities of Principal Component Analysis (PCA) for dimensionality reduction and feature extraction with the Kolmogorov–Smirnov (KS)-based scheme for fault detection. The KS indicator is computed between the two distributions in a moving window of fixed length, allowing it to capture sensitive details that enhance the detection of faults. Moreover, no labeling is required when using this fault detection approach, making it flexible in practice. The performance of the proposed PCA–KS strategy is assessed for different sensor faults on benchmark processes, specifically the Plug Flow Reactor (PFR) process and the benchmark Tennessee Eastman (TE) process. Different sensor faults, including bias, intermittent, and aging faults, are considered in this study to evaluate the proposed fault detection scheme. The results demonstrate that the proposed approach surpasses traditional PCA-based methods. Specifically, when applied to PFR data, it achieves a high average detection rate of 98.31% and a low false alarm rate of 0.25%. Similarly, when applied to the TE process, it provides a good average detection rate of 97.27% and a false alarm rate of 6.32%. These results underscore the efficacy of the proposed PCA–KS approach in enhancing the fault detection of high-dimensional processes. |
| first_indexed | 2024-03-07T22:38:03Z |
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| id | doaj.art-d26020b707d846c8badc15b7726a6bce |
| institution | Directory Open Access Journal |
| issn | 2305-7084 |
| language | English |
| last_indexed | 2024-03-07T22:38:03Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ChemEngineering |
| spelling | doaj.art-d26020b707d846c8badc15b7726a6bce2024-02-23T15:12:01ZengMDPI AGChemEngineering2305-70842023-12-0181110.3390/chemengineering8010001Improved Fault Detection in Chemical Engineering Processes via Non-Parametric Kolmogorov–Smirnov-Based Monitoring StrategyK. Ramakrishna Kini0Muddu Madakyaru1Fouzi Harrou2Mukund Kumar Menon3Ying Sun4Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, IndiaDepartment of Chemical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, IndiaElectrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST) Computer, Thuwal 23955-6900, Saudi ArabiaDepartment of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, IndiaElectrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST) Computer, Thuwal 23955-6900, Saudi ArabiaFault detection is crucial in maintaining reliability, safety, and consistent product quality in chemical engineering processes. Accurate fault detection allows for identifying anomalies, signaling deviations from the system’s nominal behavior, ensuring the system operates within desired performance parameters, and minimizing potential losses. This paper presents a novel semi-supervised data-based monitoring technique for fault detection in multivariate processes. To this end, the proposed approach merges the capabilities of Principal Component Analysis (PCA) for dimensionality reduction and feature extraction with the Kolmogorov–Smirnov (KS)-based scheme for fault detection. The KS indicator is computed between the two distributions in a moving window of fixed length, allowing it to capture sensitive details that enhance the detection of faults. Moreover, no labeling is required when using this fault detection approach, making it flexible in practice. The performance of the proposed PCA–KS strategy is assessed for different sensor faults on benchmark processes, specifically the Plug Flow Reactor (PFR) process and the benchmark Tennessee Eastman (TE) process. Different sensor faults, including bias, intermittent, and aging faults, are considered in this study to evaluate the proposed fault detection scheme. The results demonstrate that the proposed approach surpasses traditional PCA-based methods. Specifically, when applied to PFR data, it achieves a high average detection rate of 98.31% and a low false alarm rate of 0.25%. Similarly, when applied to the TE process, it provides a good average detection rate of 97.27% and a false alarm rate of 6.32%. These results underscore the efficacy of the proposed PCA–KS approach in enhancing the fault detection of high-dimensional processes.https://www.mdpi.com/2305-7084/8/1/1fault detectiondata drivendimensionality reductionKolmogorov–Smirnov indicatorPlug-Flow ReactorTennessee Eastman process |
| spellingShingle | K. Ramakrishna Kini Muddu Madakyaru Fouzi Harrou Mukund Kumar Menon Ying Sun Improved Fault Detection in Chemical Engineering Processes via Non-Parametric Kolmogorov–Smirnov-Based Monitoring Strategy ChemEngineering fault detection data driven dimensionality reduction Kolmogorov–Smirnov indicator Plug-Flow Reactor Tennessee Eastman process |
| title | Improved Fault Detection in Chemical Engineering Processes via Non-Parametric Kolmogorov–Smirnov-Based Monitoring Strategy |
| title_full | Improved Fault Detection in Chemical Engineering Processes via Non-Parametric Kolmogorov–Smirnov-Based Monitoring Strategy |
| title_fullStr | Improved Fault Detection in Chemical Engineering Processes via Non-Parametric Kolmogorov–Smirnov-Based Monitoring Strategy |
| title_full_unstemmed | Improved Fault Detection in Chemical Engineering Processes via Non-Parametric Kolmogorov–Smirnov-Based Monitoring Strategy |
| title_short | Improved Fault Detection in Chemical Engineering Processes via Non-Parametric Kolmogorov–Smirnov-Based Monitoring Strategy |
| title_sort | improved fault detection in chemical engineering processes via non parametric kolmogorov smirnov based monitoring strategy |
| topic | fault detection data driven dimensionality reduction Kolmogorov–Smirnov indicator Plug-Flow Reactor Tennessee Eastman process |
| url | https://www.mdpi.com/2305-7084/8/1/1 |
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