Automatic detection of hardware failures in an air quality measuring station with low cost sensors
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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.
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Low-Cost Sensor, Air pollution Monitoring, Hardware reliability, Detection, Diagnostic
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
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