Automatic detection of hardware failures in an air quality measuring station with low cost sensors

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Sep 4, 2023
Sylvain Poupry Kamal Medjaher Cédrick Béler

Abstract

Monitoring air quality to protect the population is a challenge for cities with modest budgets. With this in mind, a measuring station has been developed using low-cost sensors (LCS) arranged in Triple Modular Redundancy (TMR). However LCS technology has limitations which lead to incomplete or inaccurate air quality measurements. To improve the availability of the measuring station, and also to make the data gathered more reliable, a fault detection method is proposed in this paper. By comparing measurements collected by the LCS in TMR configuration, the proposed method synthesizes measurements for each monitored parameter and assesses the health state of the measuring station in real-time. This information can be used to promptly alert maintenance teams, facilitating timely interventions and ensuring the continuous monitoring of air quality.  

Abstract 108 | PDF Downloads 117

##plugins.themes.bootstrap3.article.details##

Keywords

Low-Cost Sensor, Air pollution Monitoring, Hardware reliability, Detection, Diagnostic

References
Bass, J., Latif-Shabgahi, G. R., & Bennett, S. (1997). Experimental comparison of voting algorithms in cases of disagreement. (Pages: 523) doi: 10.1109/EURMIC.1997.617368

Castell, N., Dauge, F. R., Schneider, P., Vogt, M., Lerner, U., Fishbain, B., . . . Bartonova, A. (2017, February). Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates? Environment International, 99, 293–302. doi: 10.1016/j.envint.2016.12.007

Kucera, P., Hyncica, O., Cidl, J., & Vasatko, J. (2006, February). Realibility model of TMR system with fault detection. IFAC Proceedings Volumes, 39(21), 468–472. doi: 10.1016/S1474-6670(17)30233-1

Lewis, A. C., Lee, J. D., Edwards, P. M., Shaw, M. D., Evans, M. J., Moller, S. J., . . . White, A. (2016, July). Evaluating the performance of low cost chemical sensors for air pollution research. Faraday Discussions, 189(0), 85–103. (Publisher: The Royal Society of Chemistry) doi: 10.1039/C5FD00201J

Leys, C., Ley, C., Klein, O., Bernard, P., & Licata, L. (2013, July). Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. Journal of Experimental Social Psychology, 49(4), 764–766. doi: 10.1016/j.jesp.2013.03.013

Lorczak, P., Caglayan, A., & Eckhardt, D. (1989, June). A theoretical investigation of generalized voters for redundant systems. In [1989] The Nineteenth International Symposium on Fault-Tolerant Computing. Digest of Papers (pp. 444–451). doi: 10.1109/FTCS.1989.105617

Morawska, L., Thai, P. K., Liu, X., Asumadu-Sakyi, A., Ayoko, G., Bartonova, A., . . . Williams, R. (2018, July). Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone? Environment International, 116, 286–299. doi: 10.1016/j.envint.2018.04.018

Muhammed, T., & Shaikh, R. A. (2017, January). An analysis of fault detection strategies in wireless sensor networks. Journal of Network and Computer Applications, 78, 267–287. doi: 10.1016/j.jnca.2016.10.019

Poupry, S., Beler, C., & Medjaher, K. (2022). Develop ́ ment of a reliable measurement station for air quality monitoring based on low-cost sensors and active redundancy. IFAC-PapersOnLine, 55(5), 7–12.

Schneider, P., Castell, N., Vogt, M., Dauge, F. R., Lahoz, W. A., & Bartonova, A. (2017, September). Mapping urban air quality in near real-time using observations from low-cost sensors and model information. Environment International, 106, 234–247. doi: 10.1016/j.envint.2017.05.005
Section
Regular Session Papers