Sensor Virtualization for Anomaly Detection of Turbo-Machinery Sensors—An Industrial Application Article Swipe
Sachin Shetty
,
V. Gori
,
Gianni Bagni
,
Giacomo Veneri
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/engproc2023039096
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/engproc2023039096
We apply a Granger causality and auto-correlation analysis to train a recurrent neural network (RNN) that acts as a virtual sensor model. These models can be used to check the status of several hundreds of sensors during turbo-machinery units’ operation. Checking the health of each sensor is a time-consuming activity. Training a supervised algorithm is not feasible because we do not know all the failure modes that the sensors can undergo. We use a semi-supervised approach and train an RNN (LSTM) on non-anomalous data to build a virtual sensor using other sensors as regressors. We use the Granger causality test to identify the set of input sensors for a given target sensor. Moreover, we look at the auto-correlation function (ACF) to understand the temporal dependency in data. We then compare the predicted signal vs. the real one to raise (in case) an anomaly in real time. Results report 96% precision and 100% recall.
Related Topics
Concepts
Computer science
Anomaly detection
Artificial intelligence
Causality (physics)
Autoregressive model
Recurrent neural network
Dependency (UML)
Autoencoder
Granger causality
Machine learning
Data mining
Pattern recognition (psychology)
Deep learning
Artificial neural network
Mathematics
Physics
Quantum mechanics
Econometrics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/engproc2023039096
- https://www.mdpi.com/2673-4591/39/1/96/pdf?version=1690448516
- OA Status
- gold
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385347484
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385347484Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/engproc2023039096Digital Object Identifier
- Title
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Sensor Virtualization for Anomaly Detection of Turbo-Machinery Sensors—An Industrial ApplicationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
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2023-07-27Full publication date if available
- Authors
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Sachin Shetty, V. Gori, Gianni Bagni, Giacomo VeneriList of authors in order
- Landing page
-
https://doi.org/10.3390/engproc2023039096Publisher landing page
- PDF URL
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https://www.mdpi.com/2673-4591/39/1/96/pdf?version=1690448516Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2673-4591/39/1/96/pdf?version=1690448516Direct OA link when available
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Computer science, Anomaly detection, Artificial intelligence, Causality (physics), Autoregressive model, Recurrent neural network, Dependency (UML), Autoencoder, Granger causality, Machine learning, Data mining, Pattern recognition (psychology), Deep learning, Artificial neural network, Mathematics, Physics, Quantum mechanics, EconometricsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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28Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.100% | 151 |
| abstract_inverted_index.acts | 16 |
| abstract_inverted_index.data | 83 |
| abstract_inverted_index.each | 44 |
| abstract_inverted_index.know | 61 |
| abstract_inverted_index.look | 114 |
| abstract_inverted_index.real | 135, 144 |
| abstract_inverted_index.test | 99 |
| abstract_inverted_index.that | 15, 66 |
| abstract_inverted_index.then | 128 |
| abstract_inverted_index.used | 26 |
| abstract_inverted_index.(ACF) | 119 |
| abstract_inverted_index.(RNN) | 14 |
| abstract_inverted_index.These | 22 |
| abstract_inverted_index.apply | 1 |
| abstract_inverted_index.build | 85 |
| abstract_inverted_index.case) | 140 |
| abstract_inverted_index.check | 28 |
| abstract_inverted_index.data. | 126 |
| abstract_inverted_index.given | 109 |
| abstract_inverted_index.input | 105 |
| abstract_inverted_index.modes | 65 |
| abstract_inverted_index.other | 90 |
| abstract_inverted_index.raise | 138 |
| abstract_inverted_index.time. | 145 |
| abstract_inverted_index.train | 9, 77 |
| abstract_inverted_index.using | 89 |
| abstract_inverted_index.(LSTM) | 80 |
| abstract_inverted_index.during | 36 |
| abstract_inverted_index.health | 42 |
| abstract_inverted_index.model. | 21 |
| abstract_inverted_index.models | 23 |
| abstract_inverted_index.neural | 12 |
| abstract_inverted_index.report | 147 |
| abstract_inverted_index.sensor | 20, 45, 88 |
| abstract_inverted_index.signal | 132 |
| abstract_inverted_index.status | 30 |
| abstract_inverted_index.target | 110 |
| abstract_inverted_index.Granger | 3, 97 |
| abstract_inverted_index.Results | 146 |
| abstract_inverted_index.anomaly | 142 |
| abstract_inverted_index.because | 57 |
| abstract_inverted_index.compare | 129 |
| abstract_inverted_index.failure | 64 |
| abstract_inverted_index.network | 13 |
| abstract_inverted_index.recall. | 152 |
| abstract_inverted_index.sensor. | 111 |
| abstract_inverted_index.sensors | 35, 68, 91, 106 |
| abstract_inverted_index.several | 32 |
| abstract_inverted_index.virtual | 19, 87 |
| abstract_inverted_index.Checking | 40 |
| abstract_inverted_index.Training | 50 |
| abstract_inverted_index.analysis | 7 |
| abstract_inverted_index.approach | 75 |
| abstract_inverted_index.feasible | 56 |
| abstract_inverted_index.function | 118 |
| abstract_inverted_index.hundreds | 33 |
| abstract_inverted_index.identify | 101 |
| abstract_inverted_index.temporal | 123 |
| abstract_inverted_index.undergo. | 70 |
| abstract_inverted_index.Moreover, | 112 |
| abstract_inverted_index.activity. | 49 |
| abstract_inverted_index.algorithm | 53 |
| abstract_inverted_index.causality | 4, 98 |
| abstract_inverted_index.precision | 149 |
| abstract_inverted_index.predicted | 131 |
| abstract_inverted_index.recurrent | 11 |
| abstract_inverted_index.dependency | 124 |
| abstract_inverted_index.operation. | 39 |
| abstract_inverted_index.supervised | 52 |
| abstract_inverted_index.understand | 121 |
| abstract_inverted_index.regressors. | 93 |
| abstract_inverted_index.non-anomalous | 82 |
| abstract_inverted_index.time-consuming | 48 |
| abstract_inverted_index.semi-supervised | 74 |
| abstract_inverted_index.turbo-machinery | 37 |
| abstract_inverted_index.auto-correlation | 6, 117 |
| abstract_inverted_index.units’ | 38 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5092560752, https://openalex.org/A5114378124, https://openalex.org/A5014605191, https://openalex.org/A5102899219 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.4000000059604645 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.09878411 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |