Graph-Driven Models for Gas Mixture Identification and Concentration Estimation on Heterogeneous Sensor Array Signals

TL;DR

This paper introduces two deep-learning models that incorporate temporal graph structures to improve the accuracy and scalability of gas mixture identification and concentration estimation using heterogeneous sensor array signals.

Contribution

The study develops two novel graph-enhanced deep learning models, GraphCapsNet and GraphANet, for improved gas analysis performance on heterogeneous datasets.

Findings

GraphCapsNet achieved over 98% accuracy in gas mixture classification.

GraphANet attained an R2 score above 0.96 in concentration estimation.

Both models outperformed recent approaches in accuracy and stability.

Abstract

Accurately identifying gas mixtures and estimating their concentrations are crucial across various industrial applications using gas sensor arrays. However, existing models face challenges in generalizing across heterogeneous datasets, which limits their scalability and practical applicability. To address this problem, this study develops two novel deep-learning models that integrate temporal graph structures for enhanced performance: a Graph-Enhanced Capsule Network (GraphCapsNet) employing dynamic routing for gas mixture classification and a Graph-Enhanced Attention Network (GraphANet) leveraging self-attention for concentration estimation. Both models were validated on datasets from the University of California, Irvine (UCI) Machine Learning Repository and a custom dataset, demonstrating superior performance in gas mixture identification and concentration estimation compared to recent models. In classification tasks, GraphCapsNet achieved over 98.00% accuracy across multiple datasets, while in concentration estimation, GraphANet attained an R2 score exceeding 0.96 across various gas components. Both GraphCapsNet and GraphANet exhibited significantly higher accuracy and stability, positioning them as promising solutions for scalable gas analysis in industrial settings.