DEVELOPMENT OF AN IOT DEVICE USING MLX90640 SENSORS FOR TEMPERATURE ACQUISITION
Keywords:
IoT, predictive maintenance, temperature sensors, tool monitoring, thermal imaging
Abstract
In this paper, a new IoT device is proposed for monitoring industrial processes. It has been developed in such a way that it is a low-cost device, uses temperature sensors and exposes the acquired data as thermal images, images that will be used as input data for prediction algorithms developed using convolutional neural networks.
References
1. S. Selcuk, “Predictive maintenance, its implementation and latest trends,” Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, vol. 231, no. 9, pp. 1670–1679, Jul. 2017, doi: 10.1177/0954405415601640.
2. M. Pech, J. Vrchota, and J. Bednář, “Predictive Maintenance and Intelligent Sensors in Smart Factory: Review,” Sensors, vol. 21, no. 4, p. 1470, Feb. 2021, doi: 10.3390/s21041470.
3. R. Gouriveau, K. Medjaher, and N. Zerhouni, From prognostics and health systems management to predictive maintenance 1: monitoring and prognostics. Hoboken, NJ: ISTE Ltd/John Wiley and Sons Inc, 2016.
4. R. K. Mobley, An introduction to predictive maintenance, 2nd ed. Amsterdam ; New York: Butterworth-Heinemann, 2002.
5. A. Grizhnevich, “A comprehensive guide to IoT-based predictive maintenance,” Science Soft, 2018. https://www.scnsoft.com/blog/iot-predictive-maintenance-guide (accessed Sep. 01, 2021).
6. “Predictive maintenance with IoT: The road to real returns,” AVNET ABACUS. https://www.avnet.com/wps/portal/abacus/solutions/markets/industrial/predictive-maintenance-iot/ (accessed Aug. 22, 2021).
7. M. Cakir, M. A. Guvenc, and S. Mistikoglu, “The experimental application of popular machine learning algorithms on predictive maintenance and the design of IIoT based condition monitoring system,” Computers & Industrial Engineering, vol. 151, p. 106948, Jan. 2021, doi: 10.1016/j.cie.2020.106948.
8. “Convolutional Neural Network,” MathWorks. https://www.mathworks.com/discovery/convolutional-neural-network-matlab.html?s_tid=srchtitle_convolutional%20neural%20network_1 (accessed Jan. 09, 2022).
9. O. Abdeljaber, O. Avci, S. Kiranyaz, M. Gabbouj, and D. J. Inman, “Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks,” Journal of Sound and Vibration, vol. 388, pp. 154–170, Feb. 2017, doi: 10.1016/j.jsv.2016.10.043.
10. S. Han, S. Zhang, Y. Li, and L. Chen, “The multilabel fault diagnosis model of bearing based on integrated convolutional neural network and gated recurrent unit,” IJICC, vol. 15, no. 3, pp. 401–413, Jul. 2022, doi: 10.1108/IJICC-08-2021-0153.
11. H. Wang, J. Xu, R. Yan, and R. X. Gao, “A New Intelligent Bearing Fault Diagnosis Method Using SDP Representation and SE-CNN,” IEEE Trans. Instrum. Meas., vol. 69, no. 5, Art. no. 5, May 2020, doi: 10.1109/TIM.2019.2956332.
12. R. F. R. Junior, I. A. dos S. Areias, M. M. Campos, C. E. Teixeira, L. E. B. da Silva, and G. F. Gomes, “Fault detection and diagnosis in electric motors using 1d convolutional neural networks with multi-channel vibration signals,” Measurement, vol. 190, p. 110759, Feb. 2022, doi: 10.1016/j.measurement.2022.110759.
13. S. Sun, B. Hu, Z. Yu, and X. Song, “A Stochastic Max Pooling Strategy for Convolutional Neural Network Trained by Noisy Samples,” INT J COMPUT COMMUN, Int. J. Comput. Commun. Control, vol. 15, no. 1, Feb. 2020, doi: 10.15837/ijccc.2020.1.3712.
14. O. G. Moldovan, “Contribuții aduse la sistemul de gestiune al sculelor în cadrul celulelor flexibile de fabricație . Aplicații la celula flexibilă de fabricație TMA 55 AL,” PhD Thesis, Oradea.
15. L. S. Csokmai, “Contribuții privind comanda ierarhizată a sistemelor flexibile de fabricație,” PhD Thesis, Oradea, 2013.
16. T. Avram, “Contribuţii privind automatizǎrile robotizate la celule flexibile din industria prelucrǎtoare a pieselor prismatice,” PhD Thesis, Oradea, 2020.
17. M. GANEA, C. BUNGAU, R. PANCU, and O. MOLDOVAN, “Constructive and technological objectives of the resources flow (working parts, tools, programs) at the flexible manufacturing cell TMA-AL-550,” ANNALS of the ORADEA UNIVERSITY, vol. Volume IX (XIX), no. Fascicle of Management and Technological Engineering, NR1, p. 3.91-3.94, 2010.
18. “Modul Cameră Termică IR Adafruit MLX90640 24x32,” Optimus Digital. https://www.optimusdigital.ro/ro/senzori-senzori-de-temperatura/11185-modul-camera-termica-ir-adafruit-mlx90640-24x32.html (accessed Aug. 10, 2022).
19. “Flir SC-320 SC-640 SC-660 High Resolution Infrared Camera for Research & Development,” Distek - Measuring Instruments. http://www.distek.ro/en/Product/Flir-SC-320-SC-640-SC-660-High-Resolution-Infrared-Camera-for-Research-and-Development--2061 (accessed Jan. 07, 2022).
2. M. Pech, J. Vrchota, and J. Bednář, “Predictive Maintenance and Intelligent Sensors in Smart Factory: Review,” Sensors, vol. 21, no. 4, p. 1470, Feb. 2021, doi: 10.3390/s21041470.
3. R. Gouriveau, K. Medjaher, and N. Zerhouni, From prognostics and health systems management to predictive maintenance 1: monitoring and prognostics. Hoboken, NJ: ISTE Ltd/John Wiley and Sons Inc, 2016.
4. R. K. Mobley, An introduction to predictive maintenance, 2nd ed. Amsterdam ; New York: Butterworth-Heinemann, 2002.
5. A. Grizhnevich, “A comprehensive guide to IoT-based predictive maintenance,” Science Soft, 2018. https://www.scnsoft.com/blog/iot-predictive-maintenance-guide (accessed Sep. 01, 2021).
6. “Predictive maintenance with IoT: The road to real returns,” AVNET ABACUS. https://www.avnet.com/wps/portal/abacus/solutions/markets/industrial/predictive-maintenance-iot/ (accessed Aug. 22, 2021).
7. M. Cakir, M. A. Guvenc, and S. Mistikoglu, “The experimental application of popular machine learning algorithms on predictive maintenance and the design of IIoT based condition monitoring system,” Computers & Industrial Engineering, vol. 151, p. 106948, Jan. 2021, doi: 10.1016/j.cie.2020.106948.
8. “Convolutional Neural Network,” MathWorks. https://www.mathworks.com/discovery/convolutional-neural-network-matlab.html?s_tid=srchtitle_convolutional%20neural%20network_1 (accessed Jan. 09, 2022).
9. O. Abdeljaber, O. Avci, S. Kiranyaz, M. Gabbouj, and D. J. Inman, “Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks,” Journal of Sound and Vibration, vol. 388, pp. 154–170, Feb. 2017, doi: 10.1016/j.jsv.2016.10.043.
10. S. Han, S. Zhang, Y. Li, and L. Chen, “The multilabel fault diagnosis model of bearing based on integrated convolutional neural network and gated recurrent unit,” IJICC, vol. 15, no. 3, pp. 401–413, Jul. 2022, doi: 10.1108/IJICC-08-2021-0153.
11. H. Wang, J. Xu, R. Yan, and R. X. Gao, “A New Intelligent Bearing Fault Diagnosis Method Using SDP Representation and SE-CNN,” IEEE Trans. Instrum. Meas., vol. 69, no. 5, Art. no. 5, May 2020, doi: 10.1109/TIM.2019.2956332.
12. R. F. R. Junior, I. A. dos S. Areias, M. M. Campos, C. E. Teixeira, L. E. B. da Silva, and G. F. Gomes, “Fault detection and diagnosis in electric motors using 1d convolutional neural networks with multi-channel vibration signals,” Measurement, vol. 190, p. 110759, Feb. 2022, doi: 10.1016/j.measurement.2022.110759.
13. S. Sun, B. Hu, Z. Yu, and X. Song, “A Stochastic Max Pooling Strategy for Convolutional Neural Network Trained by Noisy Samples,” INT J COMPUT COMMUN, Int. J. Comput. Commun. Control, vol. 15, no. 1, Feb. 2020, doi: 10.15837/ijccc.2020.1.3712.
14. O. G. Moldovan, “Contribuții aduse la sistemul de gestiune al sculelor în cadrul celulelor flexibile de fabricație . Aplicații la celula flexibilă de fabricație TMA 55 AL,” PhD Thesis, Oradea.
15. L. S. Csokmai, “Contribuții privind comanda ierarhizată a sistemelor flexibile de fabricație,” PhD Thesis, Oradea, 2013.
16. T. Avram, “Contribuţii privind automatizǎrile robotizate la celule flexibile din industria prelucrǎtoare a pieselor prismatice,” PhD Thesis, Oradea, 2020.
17. M. GANEA, C. BUNGAU, R. PANCU, and O. MOLDOVAN, “Constructive and technological objectives of the resources flow (working parts, tools, programs) at the flexible manufacturing cell TMA-AL-550,” ANNALS of the ORADEA UNIVERSITY, vol. Volume IX (XIX), no. Fascicle of Management and Technological Engineering, NR1, p. 3.91-3.94, 2010.
18. “Modul Cameră Termică IR Adafruit MLX90640 24x32,” Optimus Digital. https://www.optimusdigital.ro/ro/senzori-senzori-de-temperatura/11185-modul-camera-termica-ir-adafruit-mlx90640-24x32.html (accessed Aug. 10, 2022).
19. “Flir SC-320 SC-640 SC-660 High Resolution Infrared Camera for Research & Development,” Distek - Measuring Instruments. http://www.distek.ro/en/Product/Flir-SC-320-SC-640-SC-660-High-Resolution-Infrared-Camera-for-Research-and-Development--2061 (accessed Jan. 07, 2022).
Published
2022-12-30
How to Cite
Noje, D., Moldovan, O., Szabolcs, C., & Melentie, A. (2022). DEVELOPMENT OF AN IOT DEVICE USING MLX90640 SENSORS FOR TEMPERATURE ACQUISITION. Nonconventional Technologies Review, 26(4). Retrieved from http://revtn.ro/index.php/revtn/article/view/400
Section
Articles
