Abnormal signal detection in gas pipes using neural networks

Hwang Ki Min, Chung Yeol Lee, Jong Seok Lee, Cheol Hoon Park

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

In this paper, we present a real-time system to detect abnormal events on gas pipes, based on the signals which are observed through the audio sensors attached on them. First, features are extracted from this signal so that they are robust to noise and invariant to the distance between a sensor and a spot at which an abnormal event like an attack on the gas pipes occurs. Then, a classifier is constructed to detect abnormal events using neural networks. It is a combination of two neural network models, a Gaussian mixture model and a multi-layer perceptron, for the reduction of miss and false alarms. The former works for miss alarm prevention and the latter for false alarm prevention. The experimental result with real data from the actual gas system shows that the propose system is effective in detecting the dangerous events in real-time having an accuracy of 92.9%.

Original languageEnglish
Title of host publicationProceedings of the 33rd Annual Conference of the IEEE Industrial Electronics Society, IECON
Pages2503-2508
Number of pages6
DOIs
Publication statusPublished - 2007
Event33rd Annual Conference of the IEEE Industrial Electronics Society, IECON - Taipei, Taiwan, Province of China
Duration: 2007 Nov 52007 Nov 8

Publication series

NameIECON Proceedings (Industrial Electronics Conference)

Other

Other33rd Annual Conference of the IEEE Industrial Electronics Society, IECON
CountryTaiwan, Province of China
CityTaipei
Period07/11/507/11/8

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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  • Cite this

    Min, H. K., Lee, C. Y., Lee, J. S., & Park, C. H. (2007). Abnormal signal detection in gas pipes using neural networks. In Proceedings of the 33rd Annual Conference of the IEEE Industrial Electronics Society, IECON (pp. 2503-2508). [4460266] (IECON Proceedings (Industrial Electronics Conference)). https://doi.org/10.1109/IECON.2007.4460266