Open AccessAnomaly detection in particulate matter sensor using hypothesis pruning generative adversarial network
Author(s) -
Park YeongHyeon,
Park Won Seok,
Kim Yeong Beom
Publication year - 2021
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.2020-0052
Subject(s) - pruning , particulates , anomaly detection , computer science , generative adversarial network , generative grammar , obstacle , artificial intelligence , data mining , pattern recognition (psychology) , deep learning , geography , agronomy , biology , ecology , archaeology
The World Health Organization provides guidelines for managing the particulate matter (PM) level because a higher PM level represents a threat to human health. To manage the PM level, a procedure for measuring the PM value is first needed. We use a PM sensor that collects the PM level by laser‐based light scattering (LLS) method because it is more cost effective than a beta attenuation monitor‐based sensor or tapered element oscillating microbalance‐based sensor. However, an LLS‐based sensor has a higher probability of malfunctioning than the higher cost sensors. In this paper, we regard the overall malfunctioning, including strange value collection or missing collection data as anomalies, and we aim to detect anomalies for the maintenance of PM measuring sensors. We propose a novel architecture for solving the above aim that we call the hypothesis pruning generative adversarial network (HP‐GAN). Through comparative experiments, we achieve AUROC and AUPRC values of 0.948 and 0.967, respectively, in the detection of anomalies in LLS‐based PM measuring sensors. We conclude that our HP‐GAN is a cutting‐edge model for anomaly detection.