A Methodology for Updating Prognostic Models via Kalman Filters Article Swipe
Venkatesh Rajagopalan
,
Arun Subramaniyan
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.36001/ijphm.2016.v7i4.2525
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.36001/ijphm.2016.v7i4.2525
Prognostic models are built to predict the future evolution of the state or health of a system. Typical applications of these models include predictions of damage (like crack, wear) andestimation of remaining useful life of a component. Prognostic models may be data based, based on known physics of the system or can be hybrid, i.e., built through a combination of data and physics. To build such models, one needs either data from the field (i.e., real-world operations) or simulations/ tests that qualitatively represent field observations. Often, field data is not easy to obtain and is limited in its availability. Thus, models are built with simulation or test data and then validated with field observations when they become available. This necessitates a procedure that allows for refinement of models to better represent real-world behavior without having to run expensive simulations or tests repeatedly. Further, a single prognostic model developed for an entire fleet may need to be updated with measurements obtained from individual units. In this paper, we describe a novel methodology, based on the Unscented Kalman Filter, that not only allows for updating such “fleet” models, but also guarantees improvement over the existing model.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.36001/ijphm.2016.v7i4.2525
- https://papers.phmsociety.org/index.php/ijphm/article/download/2525/1485
- OA Status
- diamond
- References
- 6
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3136008740
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https://openalex.org/W3136008740Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.36001/ijphm.2016.v7i4.2525Digital Object Identifier
- Title
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A Methodology for Updating Prognostic Models via Kalman FiltersWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-11-13Full publication date if available
- Authors
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Venkatesh Rajagopalan, Arun SubramaniyanList of authors in order
- Landing page
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https://doi.org/10.36001/ijphm.2016.v7i4.2525Publisher landing page
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https://papers.phmsociety.org/index.php/ijphm/article/download/2525/1485Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://papers.phmsociety.org/index.php/ijphm/article/download/2525/1485Direct OA link when available
- Concepts
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Kalman filter, Field (mathematics), Computer science, Component (thermodynamics), Data mining, Artificial intelligence, Mathematics, Pure mathematics, Thermodynamics, PhysicsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.necessitates | 119 |
| abstract_inverted_index.observations | 113 |
| abstract_inverted_index.simulations/ | 78 |
| abstract_inverted_index.andestimation | 29 |
| abstract_inverted_index.availability. | 98 |
| abstract_inverted_index.observations. | 84 |
| abstract_inverted_index.qualitatively | 81 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.17276852 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |