An Adaptive GViT for Gas Mixture Identification and Concentration Estimation
This addresses industrial gas safety by improving real-time monitoring with variable-length data, though it appears incremental as it builds on existing GCN and ViT methods for a specific domain bottleneck.
The paper tackles the problem of gas mixture identification and concentration estimation in industrial settings, where existing algorithms struggle with variable-length sensor data, and proposes a GCN-ViT (GViT) model that achieves 97.61% accuracy in gas identification and R2 scores above 99.5% for pure gas and above 95% for mixed gas concentration estimation.
Estimating the composition and concentration of ambient gases is crucial for industrial gas safety. Even though other researchers have proposed some gas identification and con-centration estimation algorithms, these algorithms still suffer from severe flaws, particularly in fulfilling industry demands. One example is that the lengths of data collected in an industrial setting tend to vary. The conventional algorithm, yet, cannot be used to analyze the variant-length data effectively. Trimming the data will preserve only steady-state values, inevitably leading to the loss of vital information. The gas identification and concentration estimation model called GCN-ViT(GViT) is proposed in this paper; we view the sensor data to be a one-way chain that has only been downscaled to retain the majority of the original in-formation. The GViT model can directly utilize sensor ar-rays' variable-length real-time signal data as input. We validated the above model on a dataset of 12-hour uninterrupted monitoring of two randomly varying gas mixtures, CO-ethylene and methane-ethylene. The accuracy of gas identification can reach 97.61%, R2 of the pure gas concentration estimation is above 99.5% on average, and R2 of the mixed gas concentration estimation is above 95% on average.