Improved Process Monitoring Strategy Using Kantorovich Distance-Independent Component Analysis: An Application to Tennessee Eastman Process
Vowing to the increasing complexity in industrial processes, the need for safety is of highest priority and this has led to development of efficient fault detection (FD) methods. Also, with rapid development of data acquisition systems, process history based methods have gained importance as their d...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9257420/ |
| _version_ | 1818876582097321984 |
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| author | K. Ramakrishna Kini Muddu Madakyaru |
| author_facet | K. Ramakrishna Kini Muddu Madakyaru |
| author_sort | K. Ramakrishna Kini |
| collection | DOAJ |
| description | Vowing to the increasing complexity in industrial processes, the need for safety is of highest priority and this has led to development of efficient fault detection (FD) methods. Also, with rapid development of data acquisition systems, process history based methods have gained importance as their dependency is on large volume of sensor data extracted from the process. The industrial data exhibits some degree of non-gaussianity for which Independent Component Analysis (ICA) technique has usually been applied in practice. Recently, a new fault indicator based on Kantorovich Distance (KD) has been proposed which computes distance between two distributions and uses the distance as an indicator of fault. The KD metric has found to provide good monitoring results for data in presence of noise and offers enhanced detection of small magnitude faults. Considering the benefits offered by KD metric, the objective of this work is to amalgamate KD metric with ICA modeling framework to have a fault detection strategy that can improve process monitoring in noisy environment. The proposed ICA-KD FD strategy is illustrated on four processes that includes Modified Continuous Stirred Tank Heater (CSTH), Tennessee Eastman (TE) process and Experimental Distillation Column Process. The simulation results indicate that the proposed FD strategy exhibits improved performance over conventional strategies while monitoring different sensor faults in noisy environment. |
| first_indexed | 2024-12-19T13:44:41Z |
| format | Article |
| id | doaj.art-b423215b10f647ba915cf094bb43d2cd |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T13:44:41Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b423215b10f647ba915cf094bb43d2cd2022-12-21T20:18:54ZengIEEEIEEE Access2169-35362020-01-01820586320587710.1109/ACCESS.2020.30377309257420Improved Process Monitoring Strategy Using Kantorovich Distance-Independent Component Analysis: An Application to Tennessee Eastman ProcessK. Ramakrishna Kini0https://orcid.org/0000-0001-8783-0610Muddu Madakyaru1https://orcid.org/0000-0002-2518-5190Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaDepartment of Chemical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaVowing to the increasing complexity in industrial processes, the need for safety is of highest priority and this has led to development of efficient fault detection (FD) methods. Also, with rapid development of data acquisition systems, process history based methods have gained importance as their dependency is on large volume of sensor data extracted from the process. The industrial data exhibits some degree of non-gaussianity for which Independent Component Analysis (ICA) technique has usually been applied in practice. Recently, a new fault indicator based on Kantorovich Distance (KD) has been proposed which computes distance between two distributions and uses the distance as an indicator of fault. The KD metric has found to provide good monitoring results for data in presence of noise and offers enhanced detection of small magnitude faults. Considering the benefits offered by KD metric, the objective of this work is to amalgamate KD metric with ICA modeling framework to have a fault detection strategy that can improve process monitoring in noisy environment. The proposed ICA-KD FD strategy is illustrated on four processes that includes Modified Continuous Stirred Tank Heater (CSTH), Tennessee Eastman (TE) process and Experimental Distillation Column Process. The simulation results indicate that the proposed FD strategy exhibits improved performance over conventional strategies while monitoring different sensor faults in noisy environment.https://ieeexplore.ieee.org/document/9257420/Process monitoringfault detectionindependent component analysisKantorovich distancesmall magnitude faultsTennessee Eastman process |
| spellingShingle | K. Ramakrishna Kini Muddu Madakyaru Improved Process Monitoring Strategy Using Kantorovich Distance-Independent Component Analysis: An Application to Tennessee Eastman Process IEEE Access Process monitoring fault detection independent component analysis Kantorovich distance small magnitude faults Tennessee Eastman process |
| title | Improved Process Monitoring Strategy Using Kantorovich Distance-Independent Component Analysis: An Application to Tennessee Eastman Process |
| title_full | Improved Process Monitoring Strategy Using Kantorovich Distance-Independent Component Analysis: An Application to Tennessee Eastman Process |
| title_fullStr | Improved Process Monitoring Strategy Using Kantorovich Distance-Independent Component Analysis: An Application to Tennessee Eastman Process |
| title_full_unstemmed | Improved Process Monitoring Strategy Using Kantorovich Distance-Independent Component Analysis: An Application to Tennessee Eastman Process |
| title_short | Improved Process Monitoring Strategy Using Kantorovich Distance-Independent Component Analysis: An Application to Tennessee Eastman Process |
| title_sort | improved process monitoring strategy using kantorovich distance independent component analysis an application to tennessee eastman process |
| topic | Process monitoring fault detection independent component analysis Kantorovich distance small magnitude faults Tennessee Eastman process |
| url | https://ieeexplore.ieee.org/document/9257420/ |
| work_keys_str_mv | AT kramakrishnakini improvedprocessmonitoringstrategyusingkantorovichdistanceindependentcomponentanalysisanapplicationtotennesseeeastmanprocess AT muddumadakyaru improvedprocessmonitoringstrategyusingkantorovichdistanceindependentcomponentanalysisanapplicationtotennesseeeastmanprocess |