Gas Sensor Array Drift in an E-Nose System: A Dataset for Machine Learning Applications

Abstract

Gas sensor arrays are widely used in various applications such as environmental monitoring, industrial process control, and medical diagnosis. However, one of the main challenges in using gas sensor arrays is their tendency to drift over time, which can significantly affect their accuracy and reliability. In this research paper, we present a gas sensor array drift dataset that can be used to evaluate and develop drift compensation techniques. The dataset consists of measurements from an array of eight metal oxide gas sensors exposed to six different target gases at varying concentrations over several months. The paper also describes the experimental setup, data acquisition process, and preliminary dataset analysis. Our results show that the sensor array exhibits significant drift over time and that the drift patterns vary depending on the target gas and concentration. This dataset can provide a valuable resource for researchers and engineers working on gas sensor array applications and can help advance the development of more robust and accurate gas sensing systems.