Detecting Anomaly in Chemical Sensors via Regularized Contrastive Learning
Diaa Badawi, Ishaan Bassi, Sule Ozev, Ahmet Enis Çetin
Abstract
In this work, we present a method for detecting anomalous chemical sensors using contrastive learning-based framework. In many practical systems, an array of multiple chemical sensors are used. Some of the sensors may malfunction due to sensor drift and chemical poisoning. In standard contrastive learning, the aim is to learn representations that will have maximum agreement among data samples of the same concept while having a minimal agreement with data samples from other concepts. In this work, we adapt standard contrastive learning to learning useful representations for out-of-distribution sample detection. Furthermore, we compare the proposed framework with the cosine similarity measure and a novel similarity measure based on the ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> norm. Our experimental results show that our approach achieves higher AUC scores (93.6%) than baseline methods (90.1%).
BibTeX
@inproceedings{icassp2022_detectinganomaly,
title = {Detecting Anomaly in Chemical Sensors via Regularized Contrastive Learning},
author = {Diaa Badawi and Ishaan Bassi and Sule Ozev and Ahmet Enis Çetin},
booktitle = {ICASSP 2022},
year = {2022}
}