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A Deep Learning Approach to the Acoustic Condition Monitoring of a Sintering Plant

Conference Paper


Abstract


  • This paper proposes the use of deep learning classification for acoustic monitoring of an industrial process. Specifically, the application is to process sound recordings to detect when additional air leaks through gaps between grate bars lining the bottom of the sinter strand pallets, caused by thermal cycling, aging and deterioration. Detecting holes is not possible visually as the hole is usually small and covered with a granular bed of sinter/blend material. Acoustic signals from normal operation and periods of air leakage are fed into the basic supervised classification methods (SVM and J48) and the deep learning networks, to learn and distinguish the differences. Results suggest that the applied deep learning approach can effectively detect the acoustic emissions from holes time segments with a minimum 79% of accuracy.

Publication Date


  • 2018

Citation


  • S. Pasha, C. Ritz, D. Stirling, P. Zulli, D. Pinson & S. Chew, "A Deep Learning Approach to the Acoustic Condition Monitoring of a Sintering Plant," in 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings, 2018, pp. 1803-1809.

Scopus Eid


  • 2-s2.0-85063505706

Start Page


  • 1803

End Page


  • 1809

Place Of Publication


  • United States

Abstract


  • This paper proposes the use of deep learning classification for acoustic monitoring of an industrial process. Specifically, the application is to process sound recordings to detect when additional air leaks through gaps between grate bars lining the bottom of the sinter strand pallets, caused by thermal cycling, aging and deterioration. Detecting holes is not possible visually as the hole is usually small and covered with a granular bed of sinter/blend material. Acoustic signals from normal operation and periods of air leakage are fed into the basic supervised classification methods (SVM and J48) and the deep learning networks, to learn and distinguish the differences. Results suggest that the applied deep learning approach can effectively detect the acoustic emissions from holes time segments with a minimum 79% of accuracy.

Publication Date


  • 2018

Citation


  • S. Pasha, C. Ritz, D. Stirling, P. Zulli, D. Pinson & S. Chew, "A Deep Learning Approach to the Acoustic Condition Monitoring of a Sintering Plant," in 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings, 2018, pp. 1803-1809.

Scopus Eid


  • 2-s2.0-85063505706

Start Page


  • 1803

End Page


  • 1809

Place Of Publication


  • United States