Real-time detection of uncalibrated sensors using Neural Networks

Nowadays, sensors play a major role in several contexts like science, industry and daily life which benefit of their use. However, the retrieved information must be reliable. Anomalies in the behavior of sensors can give rise to critical consequences such as ruining a scientific project or jeopardiz...

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Autores: Muñoz-Molina, Luis J., Cazorla-Piñar, Ignacio, Domínguez Morales, Juan Pedro, Pérez-Peña, Fernando
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/115220
Acceso en línea:https://hdl.handle.net/11441/115220
Access Level:acceso abierto
Palabra clave:Neural Networks
sensors
uncalibrations
sensor anomalies
transfer learning
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spelling Real-time detection of uncalibrated sensors using Neural NetworksMuñoz-Molina, Luis J.Cazorla-Piñar, IgnacioDomínguez Morales, Juan PedroPérez-Peña, FernandoNeural Networkssensorsuncalibrationssensor anomaliestransfer learningNowadays, sensors play a major role in several contexts like science, industry and daily life which benefit of their use. However, the retrieved information must be reliable. Anomalies in the behavior of sensors can give rise to critical consequences such as ruining a scientific project or jeopardizing the quality of the production in industrial production lines. One of the more subtle kind of anomalies are uncalibrations. An uncalibration is said to take place when the sensor is not adjusted or standardized by calibration according to a ground truth value. In this work, an online machine-learning based uncalibration detector for temperature, humidity and pressure sensors was developed. This solution integrates an Artificial Neural Network as main component which learns from the behavior of the sensors under calibrated conditions. Then, after trained and deployed, it detects uncalibrations once they take place. The obtained results show that the proposed solution is able to detect uncalibrations for deviation values of 0.25º, 1% RH and 1.5 Pa, respectively. This solution can be adapted to different contexts by means of transfer learning, whose application allows for the addition of new sensors, the deployment into new environments and the retraining of the model with minimum amounts of data.Ministerio de Ciencia, Innovación y Universidades PID2019-105556GB-C33Cornell UniversityArquitectura y Tecnología de ComputadoresTEP-108: Robótica y Tecnología de ComputadoresMinisterio de Ciencia, Innovación y Universidades (MICINN). España2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/115220reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésarXiv.org, arXiv:2102.01565, 1-23.PID2019-105556GB-C33https://arxiv.org/abs/2102.01565info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1152202026-06-17T12:51:07Z
dc.title.none.fl_str_mv Real-time detection of uncalibrated sensors using Neural Networks
title Real-time detection of uncalibrated sensors using Neural Networks
spellingShingle Real-time detection of uncalibrated sensors using Neural Networks
Muñoz-Molina, Luis J.
Neural Networks
sensors
uncalibrations
sensor anomalies
transfer learning
title_short Real-time detection of uncalibrated sensors using Neural Networks
title_full Real-time detection of uncalibrated sensors using Neural Networks
title_fullStr Real-time detection of uncalibrated sensors using Neural Networks
title_full_unstemmed Real-time detection of uncalibrated sensors using Neural Networks
title_sort Real-time detection of uncalibrated sensors using Neural Networks
dc.creator.none.fl_str_mv Muñoz-Molina, Luis J.
Cazorla-Piñar, Ignacio
Domínguez Morales, Juan Pedro
Pérez-Peña, Fernando
author Muñoz-Molina, Luis J.
author_facet Muñoz-Molina, Luis J.
Cazorla-Piñar, Ignacio
Domínguez Morales, Juan Pedro
Pérez-Peña, Fernando
author_role author
author2 Cazorla-Piñar, Ignacio
Domínguez Morales, Juan Pedro
Pérez-Peña, Fernando
author2_role author
author
author
dc.contributor.none.fl_str_mv Arquitectura y Tecnología de Computadores
TEP-108: Robótica y Tecnología de Computadores
Ministerio de Ciencia, Innovación y Universidades (MICINN). España
dc.subject.none.fl_str_mv Neural Networks
sensors
uncalibrations
sensor anomalies
transfer learning
topic Neural Networks
sensors
uncalibrations
sensor anomalies
transfer learning
description Nowadays, sensors play a major role in several contexts like science, industry and daily life which benefit of their use. However, the retrieved information must be reliable. Anomalies in the behavior of sensors can give rise to critical consequences such as ruining a scientific project or jeopardizing the quality of the production in industrial production lines. One of the more subtle kind of anomalies are uncalibrations. An uncalibration is said to take place when the sensor is not adjusted or standardized by calibration according to a ground truth value. In this work, an online machine-learning based uncalibration detector for temperature, humidity and pressure sensors was developed. This solution integrates an Artificial Neural Network as main component which learns from the behavior of the sensors under calibrated conditions. Then, after trained and deployed, it detects uncalibrations once they take place. The obtained results show that the proposed solution is able to detect uncalibrations for deviation values of 0.25º, 1% RH and 1.5 Pa, respectively. This solution can be adapted to different contexts by means of transfer learning, whose application allows for the addition of new sensors, the deployment into new environments and the retraining of the model with minimum amounts of data.
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/115220
url https://hdl.handle.net/11441/115220
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv arXiv.org, arXiv:2102.01565, 1-23.
PID2019-105556GB-C33
https://arxiv.org/abs/2102.01565
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Cornell University
publisher.none.fl_str_mv Cornell University
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300719

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