naa a22 4500 510820239 CHVBK 20180411083528.0 cr unu---uuuuu 180411e20130301xx s 000 0 eng 10.1007/s11814-012-0197-7 doi (NATIONALLICENCE)springer-10.1007/s11814-012-0197-7 Adaptive neuro-fuzzy inference system based faulty sensor monitoring of indoor air quality in a subway station [Elektronische Daten] [Hongbin Liu, Mingzhi Huang, Jeong Kim, ChangKyoo Yoo] A new faulty sensor monitoring method based on an adaptive neuro-fuzzy inference system (ANFIS) is proposed to improve the monitoring performance of indoor air quality (IAQ) in subway stations. To enhance network performance, a data preprocessing step for detecting outliers and treating missing data is implemented before building the monitoring models. A squared prediction error (SPE) monitoring index based on the ANFIS prediction model is proposed to detect sensor faults, where the confidence limit for the SPE index is determined by using the kernel density estimation method. The proposed monitoring approach is applied to detect four typical kinds of sensor faults that may happen in the indoor space of a subway. The prediction results in the subway system indicate that the prediction accuracy of an ANFIS structure with 15 clusters is superior to that of an appropriate artificial neural network structure. Specifically, when detecting one kind of complete failure fault that happened within the normal range, the detection performance of ANFIS-based SPE outperforms that of a traditional principal component analysis method. The developed sensor monitoring technique could work well for other kinds of sensor faults resulting from a noxious underground environment. Korean Institute of Chemical Engineers, Seoul, Korea, 2012 Adaptive Neuro-fuzzy Inference System (ANFIS) nationallicence Indoor Air Quality nationallicence Kernel Density Estimation nationallicence Sensor Fault Detection nationallicence Subway Systems nationallicence Liu Hongbin Department of Environmental Science and Engineering, Center for Environmental Studies, Kyung Hee University, Seocheon-dong 1, Giheung-gu, Yongin-si, 446-701, Gyeonggi-do, Korea aut Huang Mingzhi College of Environmental Science and Engineering, South China University of Technology, 510640, Guangzhou, P. R. China aut Kim Jeong Department of Architectural Engineering, Kyung Hee University, Seocheon-dong 1, Giheung-gu, Yongin-si, 446-701, Gyeonggi-do, Korea aut Yoo ChangKyoo Department of Environmental Science and Engineering, Center for Environmental Studies, Kyung Hee University, Seocheon-dong 1, Giheung-gu, Yongin-si, 446-701, Gyeonggi-do, Korea aut Korean Journal of Chemical Engineering Springer US; http://www.springer-ny.com 30/3(2013-03-01), 528-539 0256-1115 30:3<528 2013 30 11814 https://doi.org/10.1007/s11814-012-0197-7 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11814-012-0197-7 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Liu Hongbin Department of Environmental Science and Engineering, Center for Environmental Studies, Kyung Hee University, Seocheon-dong 1, Giheung-gu, Yongin-si, 446-701, Gyeonggi-do, Korea aut NATIONALLICENCE 700 1- Huang Mingzhi College of Environmental Science and Engineering, South China University of Technology, 510640, Guangzhou, P. R. China aut NATIONALLICENCE 700 1- Kim Jeong Department of Architectural Engineering, Kyung Hee University, Seocheon-dong 1, Giheung-gu, Yongin-si, 446-701, Gyeonggi-do, Korea aut NATIONALLICENCE 700 1- Yoo ChangKyoo Department of Environmental Science and Engineering, Center for Environmental Studies, Kyung Hee University, Seocheon-dong 1, Giheung-gu, Yongin-si, 446-701, Gyeonggi-do, Korea aut NATIONALLICENCE 773 0- Korean Journal of Chemical Engineering Springer US; http://www.springer-ny.com 30/3(2013-03-01), 528-539 0256-1115 30:3<528 2013 30 11814 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer