Detecting Gas Vapor Leaks Using Uncalibrated Sensors
This work addresses gas leak detection for safety and environmental monitoring, but it appears incremental as it compares existing and hybrid methods without claiming major breakthroughs.
The paper tackled the problem of detecting gas vapor leaks using uncalibrated sensors by applying three deep neural network algorithms to time-series data from infra-red and E-nose sensors, resulting in empirical comparisons of their effectiveness.
Chemical and infra-red sensors generate distinct responses under similar conditions because of sensor drift, noise or resolution errors. In this work, we use different time-series data sets obtained by infra-red and E-nose sensors in order to detect Volatile Organic Compounds (VOCs) and Ammonia vapor leaks. We process time-series sensor signals using deep neural networks (DNN). Three neural network algorithms are utilized for this purpose. Additive neural networks (termed AddNet) are based on a multiplication-devoid operator and consequently exhibit energy-efficiency compared to regular neural networks. The second algorithm uses generative adversarial neural networks so as to expose the classifying neural network to more realistic data points in order to help the classifier network to deliver improved generalization. Finally, we use conventional convolutional neural networks as a baseline method and compare their performance with the two aforementioned deep neural network algorithms in order to evaluate their effectiveness empirically.