Anomaly Detection in Particulate Matter Sensor using Hypothesis Pruning Generative Adversarial Network
This work addresses the maintenance of cost-effective air quality sensors to ensure accurate PM level measurements, but it appears incremental as it builds on existing GAN methods for anomaly detection.
The paper tackles the problem of detecting malfunctions in particulate matter sensors, which are crucial for monitoring air quality, by proposing a Hypothesis Pruning Generative Adversarial Network (HP-GAN) and shows it performs better than other anomaly detection architectures in experiments.
World Health Organization (WHO) provides the guideline for managing the Particulate Matter (PM) level because when the PM level is higher, it threats the human health. For managing PM level, the procedure for measuring PM value is needed firstly. We use Tapered Element Oscillating Microbalance (TEOM)-based PM measuring sensors because it shows higher cost-effectiveness than Beta Attenuation Monitor (BAM)-based sensor. However, TEOM-based sensor has higher probability of malfunctioning than BAM-based sensor. In this paper, we call the overall malfunction as an anomaly, and we aim to detect anomalies for the maintenance of PM measuring sensors. We propose a novel architecture for solving the above aim that named as Hypothesis Pruning Generative Adversarial Network (HP-GAN). We experimentally compare the several anomaly detection architectures to certify ours performing better.