Sensor Fault Accommodation for a Plug Flow Reactor using an M-Estimator Article Swipe
Gaurav Seth
,
Pavanraj H. Rangegowda
,
Sachin C. Patwardhan
,
Mani Bhushan
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.ifacol.2022.07.532
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.ifacol.2022.07.532
The presence of gross errors in the measurements can lead to biased state estimates when conventional Bayesian estimators are used. This can hamper the model-based monitoring and control schemes that rely on the accurate state estimates. In this work, we have developed a framework for robust estimation of the state profiles for Distributed parameter systems (DPSs), in the presence of biased measurements. The proposed approach uses an M-estimator to identify the faulty sensor. The sensor fault diagnosis is then used to augment the state estimator with an extra state that estimates the drifting sensor bias. The proposed approach has been applied to an Auto-Thermal tubular reactor system. The proposed scheme successfully isolates the biased temperature sensors and includes or removes additional bias states as and when required. The gross errors/biases are estimated and subsequently accommodated to provide accurate estimates of spatial profiles of reactor concentration and temperature.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.ifacol.2022.07.532
- OA Status
- diamond
- Cited By
- 2
- References
- 9
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4289878043Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.ifacol.2022.07.532Digital Object Identifier
- Title
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Sensor Fault Accommodation for a Plug Flow Reactor using an M-EstimatorWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-01-01Full publication date if available
- Authors
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Gaurav Seth, Pavanraj H. Rangegowda, Sachin C. Patwardhan, Mani BhushanList of authors in order
- Landing page
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https://doi.org/10.1016/j.ifacol.2022.07.532Publisher landing page
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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://doi.org/10.1016/j.ifacol.2022.07.532Direct OA link when available
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Estimator, Bayesian probability, Computer science, Control theory (sociology), Fault (geology), State (computer science), Algorithm, Mathematics, Statistics, Artificial intelligence, Control (management), Geology, SeismologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2024: 2Per-year citation counts (last 5 years)
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9Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.provide | 136 |
| abstract_inverted_index.reactor | 105, 143 |
| abstract_inverted_index.removes | 119 |
| abstract_inverted_index.schemes | 28 |
| abstract_inverted_index.sensor. | 72 |
| abstract_inverted_index.sensors | 115 |
| abstract_inverted_index.spatial | 140 |
| abstract_inverted_index.system. | 106 |
| abstract_inverted_index.systems | 54 |
| abstract_inverted_index.tubular | 104 |
| abstract_inverted_index.Bayesian | 16 |
| abstract_inverted_index.accurate | 33, 137 |
| abstract_inverted_index.approach | 64, 97 |
| abstract_inverted_index.drifting | 92 |
| abstract_inverted_index.identify | 69 |
| abstract_inverted_index.includes | 117 |
| abstract_inverted_index.isolates | 111 |
| abstract_inverted_index.presence | 1, 58 |
| abstract_inverted_index.profiles | 50, 141 |
| abstract_inverted_index.proposed | 63, 96, 108 |
| abstract_inverted_index.developed | 41 |
| abstract_inverted_index.diagnosis | 76 |
| abstract_inverted_index.estimated | 131 |
| abstract_inverted_index.estimates | 13, 90, 138 |
| abstract_inverted_index.estimator | 84 |
| abstract_inverted_index.framework | 43 |
| abstract_inverted_index.parameter | 53 |
| abstract_inverted_index.required. | 126 |
| abstract_inverted_index.additional | 120 |
| abstract_inverted_index.estimates. | 35 |
| abstract_inverted_index.estimation | 46 |
| abstract_inverted_index.estimators | 17 |
| abstract_inverted_index.monitoring | 25 |
| abstract_inverted_index.Distributed | 52 |
| abstract_inverted_index.M-estimator | 67 |
| abstract_inverted_index.model-based | 24 |
| abstract_inverted_index.temperature | 114 |
| abstract_inverted_index.Auto-Thermal | 103 |
| abstract_inverted_index.accommodated | 134 |
| abstract_inverted_index.conventional | 15 |
| abstract_inverted_index.measurements | 7 |
| abstract_inverted_index.subsequently | 133 |
| abstract_inverted_index.successfully | 110 |
| abstract_inverted_index.temperature. | 146 |
| abstract_inverted_index.concentration | 144 |
| abstract_inverted_index.errors/biases | 129 |
| abstract_inverted_index.measurements. | 61 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 94 |
| corresponding_author_ids | https://openalex.org/A5059178238 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I162827531 |
| citation_normalized_percentile.value | 0.52260703 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |