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

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.