Gas sensing techniques are often required to not only be sensitive and portable, but also to be able to identify gases. In this paper, we describe and demonstrate a new gases identification approach based on noise spectroscopy. By using the model of the gas sensor noise developed in our previous work, we calculate the exact theoretical expression of the first derivative of the power spectral density (PSD) of the gas sensor noise, and we show that there is a correlation between this expression and the nature of the detected gas. This theoretical result is argued by some experimental results performed on metal oxide (MOX) gas sensor exposed to various gases. The new principle holds prospects for finding powerful method for identification of gases.

Fuel cells are key elements in the transition to clean energy thanks to their neutral carbon footprint, as well as their great capacity for the generation of electrical energy by oxidizing hydrogen. However, these cells operate under straining conditions of temperature and humidity that favor degradation processes. Furthermore, the presence of hydrogen—a highly flammable gas—renders the assessment of their degradations and failures crucial to the safety of their use. This paper deals with the combination of physical knowledge and data analysis for the identification of health indices (HIs) that carry information on the degradation process of fuel cells. Then, a failure prognosis method is achieved through the trend modeling of the identified HI using a data-driven and updatable state model. Finally, the remaining useful life is predicted through the calculation of the times of crossing of the predicted HI and the failure threshold. The trend model is updated when the estimation error between the predicted and measured values of the HI surpasses a predefined threshold to guarantee the adaptation of the prediction to changes in the operating conditions of the system. The effectiveness of the proposed approach is demonstrated by evaluating the obtained experimental results with prognosis performance analysis techniques.

Metal oxide gas sensors have enabled gas and vapor detection with high sensitivity. However, this type of sensor suffer from lack of selectivity. Noise spectroscopy is one of solution to improve the selectivity. In this paper, we propose a technique based on using low frequency noise to extract new features, which form unique gas signatures. By applying the recent developed noise spectroscopy-based gas identifying methods, we show the possibility of each gas signatures to discriminate gases. Noise measurements have been performed on tungsten trioxide (WO3) based gas sensor exposed to several NO2, and O3 concentrations in dry air. The obtained results have demonstrated that a selective sensing of the studied gases is possible using a single MOX gas micro-sensor.