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Mining Publication: Neural Network Application to Mine-Fire Diesel-Exhaust Discrimination

NOTE: This page is archived for historical purposes and is no longer being maintained or updated.

Original creation date: October 2002

Image of publication Neural Network Application to Mine-Fire Diesel-Exhaust Discrimination

A series of seven underground-coal-mine fire experiments was conducted in the Safety Research Coal Mine at the National Institute for Occupational Safety and Health, Pittsburgh Research Laboratory. Coal and styrene-butadiene-rubber conveyor belting were burned upwind of two sensor stations, 18 m and 148 m from the fire source. Exhaust from a diesel locomotive flowed over the fire sources in six of the tests. Metal-oxide-semiconductor (MOS), CO, and optical-path smoke sensors were positioned at both stations and found to be an optimum set of sensors for the fire discriminations. A representative set of 7,679 samples of CO data and data from the smoke and diesel-exhaust MOS sensors were used as inputs to train a neural network (NN). By testing 42,538 data samples from the seven experiments, all fires were detected by the NN within 9.67 min from the onset of significant changes in the MOS voltages without any false alarms.

Authors: GF Friel, JC Edwards

Conference Paper - October 2002

  • 0.28 MB

NIOSHTIC2 Number: 20023130

In: De Souze E, Ed. Proceedings of the North American/Ninth U.S. Mine Ventilation Symposium (Jun 8-12, 2002; Kingston, Ontario, Canada). A. A. Balkema Publishers, Lisse, Netherlands; :533-538