Detecting Gas Vapor Leaks Using Uncalibrated Sensors

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

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


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.

Original languageEnglish (US)
Article number8883150
Pages (from-to)155701-155710
Number of pages10
JournalIEEE Access
StatePublished - 2019


  • VOC gas leak detection
  • additive
  • and generative adversarial (GAN) neural networks
  • convolutional
  • sensor drift
  • time-series data analysis

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


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