Optimal Sensor Placement for Reliable Virtual Sensing Using Modal Expansion and Information Theory
A framework for optimal sensor placement (OSP) for virtual sensing using the modal expansion technique and taking into account uncertainties is presented based on information and utility theory. The framework is developed to handle virtual sensing under output-only vibration measurements. The OSP ma...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/10/3400 |
| _version_ | 1797534233494814720 |
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| author | Tulay Ercan Costas Papadimitriou |
| author_facet | Tulay Ercan Costas Papadimitriou |
| author_sort | Tulay Ercan |
| collection | DOAJ |
| description | A framework for optimal sensor placement (OSP) for virtual sensing using the modal expansion technique and taking into account uncertainties is presented based on information and utility theory. The framework is developed to handle virtual sensing under output-only vibration measurements. The OSP maximizes a utility function that quantifies the expected information gained from the data for reducing the uncertainty of quantities of interest (QoI) predicted at the virtual sensing locations. The utility function is extended to make the OSP design robust to uncertainties in structural model and modeling error parameters, resulting in a multidimensional integral of the expected information gain over all possible values of the uncertain parameters and weighted by their assigned probability distributions. Approximate methods are used to compute the multidimensional integral and solve the optimization problem that arises. The Gaussian nature of the response QoI is exploited to derive useful and informative analytical expressions for the utility function. A thorough study of the effect of model, prediction and measurement errors and their uncertainties, as well as the prior uncertainties in the modal coordinates on the selection of the optimal sensor configuration is presented, highlighting the importance of accounting for robustness to errors and other uncertainties. |
| first_indexed | 2024-03-10T11:26:46Z |
| format | Article |
| id | doaj.art-5475213afb384f5da545750c2aa21545 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T11:26:46Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5475213afb384f5da545750c2aa215452023-11-21T19:34:06ZengMDPI AGSensors1424-82202021-05-012110340010.3390/s21103400Optimal Sensor Placement for Reliable Virtual Sensing Using Modal Expansion and Information TheoryTulay Ercan0Costas Papadimitriou1Department of Mechanical Engineering, University of Thessaly, Pedion Areos, 383 34 Volos, GreeceDepartment of Mechanical Engineering, University of Thessaly, Pedion Areos, 383 34 Volos, GreeceA framework for optimal sensor placement (OSP) for virtual sensing using the modal expansion technique and taking into account uncertainties is presented based on information and utility theory. The framework is developed to handle virtual sensing under output-only vibration measurements. The OSP maximizes a utility function that quantifies the expected information gained from the data for reducing the uncertainty of quantities of interest (QoI) predicted at the virtual sensing locations. The utility function is extended to make the OSP design robust to uncertainties in structural model and modeling error parameters, resulting in a multidimensional integral of the expected information gain over all possible values of the uncertain parameters and weighted by their assigned probability distributions. Approximate methods are used to compute the multidimensional integral and solve the optimization problem that arises. The Gaussian nature of the response QoI is exploited to derive useful and informative analytical expressions for the utility function. A thorough study of the effect of model, prediction and measurement errors and their uncertainties, as well as the prior uncertainties in the modal coordinates on the selection of the optimal sensor configuration is presented, highlighting the importance of accounting for robustness to errors and other uncertainties.https://www.mdpi.com/1424-8220/21/10/3400information gainKullback-Leibler divergencerelative entropyBayesian inferenceresponse predictions |
| spellingShingle | Tulay Ercan Costas Papadimitriou Optimal Sensor Placement for Reliable Virtual Sensing Using Modal Expansion and Information Theory Sensors information gain Kullback-Leibler divergence relative entropy Bayesian inference response predictions |
| title | Optimal Sensor Placement for Reliable Virtual Sensing Using Modal Expansion and Information Theory |
| title_full | Optimal Sensor Placement for Reliable Virtual Sensing Using Modal Expansion and Information Theory |
| title_fullStr | Optimal Sensor Placement for Reliable Virtual Sensing Using Modal Expansion and Information Theory |
| title_full_unstemmed | Optimal Sensor Placement for Reliable Virtual Sensing Using Modal Expansion and Information Theory |
| title_short | Optimal Sensor Placement for Reliable Virtual Sensing Using Modal Expansion and Information Theory |
| title_sort | optimal sensor placement for reliable virtual sensing using modal expansion and information theory |
| topic | information gain Kullback-Leibler divergence relative entropy Bayesian inference response predictions |
| url | https://www.mdpi.com/1424-8220/21/10/3400 |
| work_keys_str_mv | AT tulayercan optimalsensorplacementforreliablevirtualsensingusingmodalexpansionandinformationtheory AT costaspapadimitriou optimalsensorplacementforreliablevirtualsensingusingmodalexpansionandinformationtheory |