@misc{10498/26123,
year = {2022},
month = {1},
url = {http://hdl.handle.net/10498/26123},
abstract = {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.},
organization = {Open Access funding provided thanks to the CRUE-CSIC
agreement with Springer Nature. This work was partially supported
by the Altran Innovation Center for Advance Manufacturing, the EU
H2020 project Virtual IoT Maintenance System (VIMS Grant ID:
878757), the Spanish grant (with support from the European Regional
Development Fund) MIND-ROB (PID2019-105556GB-C33) and by
the EU H2020 project CHIST-ERA SMALL (PCI2019-111841-2).},
publisher = {Springer},
keywords = {Neural networks},
keywords = {Sensors},
keywords = {Uncalibrations},
keywords = {Sensor anomalies},
keywords = {Transfer learning},
title = {Real-time detection of uncalibrated sensors using neural networks},
doi = {10.1007/s00521-021-06865-z},
author = {Muñoz Molina, Luis J. and Cazorla Piñar, Ignacio and Domínguez Morales, Juan P. and Lafuente Molinero, Luis and Pérez Peña, Fernando},
}