Sensor Drift Compensation in Electronic-Nose-Based Gas Recognition Using Knowledge Distillation

TL;DR

This paper introduces a novel knowledge distillation approach for sensor drift compensation in electronic nose gas recognition, demonstrating significant accuracy improvements over existing methods through rigorous statistical validation.

Contribution

The study presents the first application of knowledge distillation for electronic nose sensor drift mitigation, outperforming traditional domain adaptation techniques with robust experimental validation.

Findings

KD outperforms DRCA and KD-DRCA in accuracy and F1-score

Up to 18% accuracy improvement with KD

Demonstrates robustness across multiple dataset partitions

Abstract

Due to environmental changes and sensor aging, sensor drift challenges the performance of electronic nose systems in gas classification during real-world deployment. Previous studies using the UCI Gas Sensor Array Drift Dataset reported promising drift compensation results but lacked robust statistical experimental validation and may overcompensate for sensor drift, losing class-related variance.To address these limitations and improve sensor drift compensation with statistical rigor, we first designed two domain adaptation tasks based on the same electronic nose dataset: using the first batch to predict the remaining batches, simulating a controlled laboratory setting; and predicting the next batch using all prior batches, simulating continuous training data updates for online training. We then systematically tested three methods: our proposed novel Knowledge Distillation (KD) method, the benchmark method Domain Regularized Component Analysis (DRCA), and a hybrid method KD-DRCA, across 30 random test set partitions on the UCI dataset. We showed that KD consistently outperformed both DRCA and KD-DRCA, achieving up to an 18% improvement in accuracy and 15% in F1-score, demonstrating KD's superior effectiveness in drift compensation. This is the first application of KD for electronic nose drift mitigation, significantly outperforming the previous state-of-the-art DRCA method and enhancing the reliability of sensor drift compensation in real-world environments.