Detecting Gas Vapor Leaks Using Uncalibrated Sensors

Diaa Badawi,Ahmet Enis Cetin,Alex Orailoglu,Sule Ozev,Tuba Ayhan,Chengmo Yang

doi:10.1109/access.2019.2949740

Abstract

Chemical and infra-red sensors generate distinct responses under similar conditions because of sensor drift, noise or resolution errors. In this paper, we develop novel machine learning methods for detecting and identifying VOC and Ammonia vapor from time-series data obtained by uncalibrated chemical and infrared sensors. 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. Our findings indicate that using raw time-series data obtained from uncalibrated sensors and processing them using deep-learning-based methods yield better results than using hand-crafted feature parameters.

Highlights

Ammonia and Volatile organic compounds (VOCs) are associated with numerous health problems
To the best of our knowledge, this is the first work that uses the raw time-series signal for VOC detection and ammonia vapor sensing using deep learning
In this paper, we have introduced a variety of deep-learning based algorithms and applied them to VOC gas and ammonia vapor leak detection and gas type identification problems

Summary

INTRODUCTION

Ammonia and Volatile organic compounds (VOCs) are associated with numerous health problems. To the best of our knowledge, this is the first work that uses the raw time-series signal for VOC detection and ammonia vapor sensing using deep learning. Neither poisoning nor aging are reversible as the physical structure of the sensor will have been permanently damaged or at the least affected The latter case is known as secondorder sensor drift and is caused by external uncontrollable environmental changes, such as temperature and humidity variations. While it is not possible to detect the concentration of the gas using MWIR and LWIR sensors in open air, it is possible to record a time-varying signal and detect the existence of gas leakage using IR sensors as shown in Fig. 3 using a machine learning algorithm such as a neural network.

DEEP LEARNING ALGORITHMS FOR IR AND CHEMICAL SENSOR DATA PROCESSING

Findings

CONCLUSION