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dLeak: An IoT-Based Gas Leak Detection Framework for Smart Factory

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Abstract

Gas industries very often suffer from leakage. This paper proposes an IoT-enabled acoustic-based leak detection system to detect leaks more accurately and rapidly that can improve the safety of such gas industries. Acoustic Emission (AE) signals are collected from a test setup of an industry, which is mimicked by a working kitchen where a pressure cooker whistle was used in the place of a leak. Each AE signal is segmented using a hamming window and features are extracted from every segment. According to our data set, a best fit model has been implemented. Naive Bayes, SVM and KNN classifiers are giving acceptable testing accuracy to detect the leaks. Signals are collected from the spot where the model has to be implemented. Then, these signals are transported to the supervisor’s device using the cloud. The signals are finally tested in the implemented model which successfully predicts the presence of a leak in that spot. The Naive Bayes, SVM and KNN models gave high testing accuracy of 92.2%, 92.4% and 94.44% respectively, of which KNN gave the best performance.

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Authors and Affiliations

  1. Department of Electrical Engineering, Jadavpur University, Kolkata, India

    Anamika Rajbanshi

  2. Department of Electronics and Communication Engineering, IIIT, Naya Raipur, India

    Debanjan Das & Rajarshi Mahapatra

  3. Department of Computer Science and Engineering Engineering, IIIT, Naya Raipur, India

    Venkanna Udutalapally

Authors
  1. Anamika Rajbanshi
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  2. Debanjan Das
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  3. Venkanna Udutalapally
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  4. Rajarshi Mahapatra
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Correspondence to Debanjan Das.

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The authors declare that they have no conflict of interest and there was no human or animal testing or participation involved in this research. Authors have collected the data experimentally.

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Rajbanshi, A., Das, D., Udutalapally, V. et al. dLeak: An IoT-Based Gas Leak Detection Framework for Smart Factory. SN COMPUT. SCI. 3, 273 (2022). https://doi.org/10.1007/s42979-022-01181-2

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