Online Parameter Estimation in Digital Twins for Real-Time Condition Monitoring
This paper introduces innovative online parameter estimation algorithms that employ both deterministic and stochastic methodologies in digital twins for real-time condition monitoring. The deterministic approach utilizes an exponential forgetting factor adaptive observer, while the stochastic approa...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10847817/ |
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| author | Agus Hasan |
| author_facet | Agus Hasan |
| author_sort | Agus Hasan |
| collection | DOAJ |
| description | This paper introduces innovative online parameter estimation algorithms that employ both deterministic and stochastic methodologies in digital twins for real-time condition monitoring. The deterministic approach utilizes an exponential forgetting factor adaptive observer, while the stochastic approach involves an adaptive Kalman filter. In contrast to conventional methods, these online algorithms demonstrate robustness against variations in initial conditions and measurement noise. Notably, the algorithms exhibit the capability to manage multiple parameters and directly estimate them from sensor measurements. The effectiveness of the proposed algorithms is demonstrated through experiments focused on parameter estimation in DC motors and marine surface vessels. The results highlight the algorithms’ accuracy in estimating parameters under diverse conditions. This research contributes to the advancement of online parameter estimation techniques for condition monitoring, showcasing their applicability and reliability in real-world scenarios involving complex systems. |
| format | Article |
| id | doaj-art-15169a1782ba4484b64a39116f03229d |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-15169a1782ba4484b64a39116f03229d2025-01-28T00:01:17ZengIEEEIEEE Access2169-35362025-01-0113147891480010.1109/ACCESS.2025.353198310847817Online Parameter Estimation in Digital Twins for Real-Time Condition MonitoringAgus Hasan0https://orcid.org/0000-0003-1434-2696Department of ICT and Natural Sciences, Norwegian University of Science and Technology, Alesund, NorwayThis paper introduces innovative online parameter estimation algorithms that employ both deterministic and stochastic methodologies in digital twins for real-time condition monitoring. The deterministic approach utilizes an exponential forgetting factor adaptive observer, while the stochastic approach involves an adaptive Kalman filter. In contrast to conventional methods, these online algorithms demonstrate robustness against variations in initial conditions and measurement noise. Notably, the algorithms exhibit the capability to manage multiple parameters and directly estimate them from sensor measurements. The effectiveness of the proposed algorithms is demonstrated through experiments focused on parameter estimation in DC motors and marine surface vessels. The results highlight the algorithms’ accuracy in estimating parameters under diverse conditions. This research contributes to the advancement of online parameter estimation techniques for condition monitoring, showcasing their applicability and reliability in real-world scenarios involving complex systems.https://ieeexplore.ieee.org/document/10847817/Digital twinsparameter estimationcondition monitoring |
| spellingShingle | Agus Hasan Online Parameter Estimation in Digital Twins for Real-Time Condition Monitoring IEEE Access Digital twins parameter estimation condition monitoring |
| title | Online Parameter Estimation in Digital Twins for Real-Time Condition Monitoring |
| title_full | Online Parameter Estimation in Digital Twins for Real-Time Condition Monitoring |
| title_fullStr | Online Parameter Estimation in Digital Twins for Real-Time Condition Monitoring |
| title_full_unstemmed | Online Parameter Estimation in Digital Twins for Real-Time Condition Monitoring |
| title_short | Online Parameter Estimation in Digital Twins for Real-Time Condition Monitoring |
| title_sort | online parameter estimation in digital twins for real time condition monitoring |
| topic | Digital twins parameter estimation condition monitoring |
| url | https://ieeexplore.ieee.org/document/10847817/ |
| work_keys_str_mv | AT agushasan onlineparameterestimationindigitaltwinsforrealtimeconditionmonitoring |