Analyzing Gas Data Using Deep Learning and 2-D Gramian Angular Fields

Abstract

The notion of employing deep learning (DL) for gas classification has kindled a revolution that has improved both data collection measures and classification performance. Yet, the current literature, with its vast contributions, has the potential in enhancing the current state of the art by employing both DL and novel visualization methods to boost classification performance and speed. Therefore, this article presents a dual classification system for high-performance gas classification: on 1-D time series data and 2-D Gramian Angular Field (GAF) data. For the GAF case study, 1-D data are converted into 2-D counterparts by means of normalization, segmentation, averaging, and color coding. The gas sensor array (GSA) dataset is used for evaluating the implemented AlexNet model for classifying 2-D GAF data and an improved version of GasNet for 1-D time-based data. Using a cloud-based architecture, the two models are evaluated and benchmarked with the state of the art. Evaluation results of the modified GasNet model on time series data signify the state-of-the-art accuracy of 96.5%, while AlexNet achieved 81.0% test accuracy of GAF classification with near real-time performance on edge computing platforms.