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@article{ART002862987},
author={Jeong-In Park and Un Gu Kang},
title={Design of particulate matter reduction algorithm by learning failure patterns of PHM-based air conditioning facilites},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2022},
volume={27},
number={7},
pages={83-92},
doi={10.9708/jksci.2022.27.07.083}

TY - JOUR
AU - Jeong-In Park
AU - Un Gu Kang
TI - Design of particulate matter reduction algorithm by learning failure patterns of PHM-based air conditioning facilites
JO - Journal of The Korea Society of Computer and Information
PY - 2022
VL - 27
IS - 7
PB - The Korean Society Of Computer And Information
SP - 83
EP - 92
SN - 1598-849X
AB - In this paper, we designed an algorithm that can control the state of PM by learning the chain failure pattern of PHM based air conditioning facility. It is an inevitable spread of PM due to the downtime caused by the failure of the air conditioning facility. The algorithm developed by us is to establish a PM management system through PHM, and it is an algorithm that maintains a constant stabilization state through learning the stop/operation pattern of the air conditioner and manages PM based on this. As a result of the simulating at a subway station for the performance qualification of the algorithm, it was verified that the concentration of PM reduces by 30% on average. In the case of stations with many passengers using the subway, the concentration of PM exceeded the Ministry of Environment Standards(100 ㎍/㎥), but it was verified that the concentration of PM was improved at all stations where the simulation was conducted. In the future research is to expand the system to comprehensively manage not only PM but also pollutants such as CO2, CO, and NO2 in subway stations.
KW - IoT;PHM;Predictive Maintenance;Pattern Learning;Particulate Matter
DO - 10.9708/jksci.2022.27.07.083
ER -

Jeong-In Park and Un Gu Kang. (2022). Design of particulate matter reduction algorithm by learning failure patterns of PHM-based air conditioning facilites. Journal of The Korea Society of Computer and Information, 27(7), 83-92.

Jeong-In Park and Un Gu Kang. 2022, "Design of particulate matter reduction algorithm by learning failure patterns of PHM-based air conditioning facilites", Journal of The Korea Society of Computer and Information, vol.27, no.7 pp.83-92. Available from: doi:10.9708/jksci.2022.27.07.083

Jeong-In Park, Un Gu Kang "Design of particulate matter reduction algorithm by learning failure patterns of PHM-based air conditioning facilites" Journal of The Korea Society of Computer and Information 27.7 pp.83-92 (2022) : 83.

Jeong-In Park, Un Gu Kang. Design of particulate matter reduction algorithm by learning failure patterns of PHM-based air conditioning facilites. 2022; 27(7), 83-92. Available from: doi:10.9708/jksci.2022.27.07.083

Jeong-In Park and Un Gu Kang. "Design of particulate matter reduction algorithm by learning failure patterns of PHM-based air conditioning facilites" Journal of The Korea Society of Computer and Information 27, no.7 (2022) : 83-92.doi: 10.9708/jksci.2022.27.07.083

Jeong-In Park; Un Gu Kang. Design of particulate matter reduction algorithm by learning failure patterns of PHM-based air conditioning facilites. Journal of The Korea Society of Computer and Information, 27(7), 83-92. doi: 10.9708/jksci.2022.27.07.083

Jeong-In Park; Un Gu Kang. Design of particulate matter reduction algorithm by learning failure patterns of PHM-based air conditioning facilites. Journal of The Korea Society of Computer and Information. 2022; 27(7) 83-92. doi: 10.9708/jksci.2022.27.07.083

Jeong-In Park, Un Gu Kang. Design of particulate matter reduction algorithm by learning failure patterns of PHM-based air conditioning facilites. 2022; 27(7), 83-92. Available from: doi:10.9708/jksci.2022.27.07.083

Jeong-In Park and Un Gu Kang. "Design of particulate matter reduction algorithm by learning failure patterns of PHM-based air conditioning facilites" Journal of The Korea Society of Computer and Information 27, no.7 (2022) : 83-92.doi: 10.9708/jksci.2022.27.07.083