Gas Classification Using Time-Series-to-Image Conversion and CNN-Based Analysis on Array Sensor

Gas detection is essential in industrial and domestic environments to ensure safety and prevent hazardous incidents. Traditional single-sensor time-series analysis often suffers from limitations in accuracy and robustness due to environmental variations. To address this issue, we propose an artificial intelligence (AI)-based approach that transforms 1-D time-series data into 2-D image representations, followed by the classification of acetylene (C2H2), ammonia (NH3), and hydrogen (H2) using convolutional neural networks (CNNs). By utilizing image transformation techniques such as recurrence plots (RPs), Gramian angular fields (GAFs), and Markov transition fields (MTFs), our method significantly enhances feature extraction from sensor data. In this study, we utilized sensor array data obtained from ZnO and CuO thin films previously synthesized using a droplet-based hydrothermal method. By exploiting the temperature-dependent response characteristics of these sensors, we aimed to improve classification accuracy. Experimental results indicate that our proposed approach achieves a 6.2% relative improvement over the LSTM baseline model (90.1%) in classification accuracy compared to the conventional LSTM model applied directly to raw time-series data. This study demonstrates that converting time-series data into image representations substantially improves gas detection performance, offering a scalable and efficient solution for various sensor-based applications. Future research will focus on real-time implementation and further optimization of deep learning architectures.