Real-time detection of uncalibrated sensors using neural networks
Nowadays, sensors play a major role in several fields, such as science, industry and everyday technology. Therefore, the information received from the sensors must be reliable. If the sensors present any anomalies, serious problems can arise, such as publishing wrong theories in scientific papers, o...
| Autores: | , , , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | España |
| Recursos: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/134847 |
| Acesso em linha: | https://hdl.handle.net/11441/134847 https://doi.org/10.1007/s00521-021-06865-z |
| Access Level: | acceso abierto |
| Palavra-chave: | Neural networks Sensors Uncalibrations Sensor anomalies Transfer learning |
| Resumo: | Nowadays, sensors play a major role in several fields, such as science, industry and everyday technology. Therefore, the information received from the sensors must be reliable. If the sensors present any anomalies, serious problems can arise, such as publishing wrong theories in scientific papers, or causing production delays in industry. One of the most common anomalies are uncalibrations. An uncalibration occurs 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 is presented. This development integrates an artificial neural network as the main component which learns from the behavior of the sensors under calibrated conditions. Then, after being trained and deployed, it detects uncalibrations once they take place. The obtained results show that the proposed system is able to detect the 100% of the presented uncalibration events, although the time response in the detection depends on the resolution of the model for the specific location, i.e., the minimum statistically significant variation in the sensor behavior that the system is able to detect. This architecture can be adapted to different contexts by applying transfer learning, such as adding new sensors or having different environments by re-training the model with minimum amount of data |
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