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Proceedings Paper

Neural networks for processing data from multiple redundant sensors for mine systems management, operation, maintenance, and control
Author(s): Aaron Gordon; Hong Chang; Robert H. King
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Paper Abstract

We have developed a neural-network approach to classifying signals by fusing information from multiple sensors. During the past three years, we have developed concepts and algorithms for an intelligent decision support system (IDSS) for mine managers. The goal of the IDSS is to detect the activities of machines in an underground coal mine and to produce management reports similar to traditional industrial engineering time studies. The data we operate on is power usage of the various machines taken every 50 milliseconds. Currently we are working with data from three machines which interact with each other: a continuous miner and two shuttle cars. Detection of events was first done using numerical techniques to arrive at locally best guesses and rule-based techniques to fuse the information from the different machines. Our current research involves dynamic recurrent neural networks (a variation of recurrent cascade correlation) which replace the numerical and rule-based techniques. Our current neural networks can accurately label approximately 90% of the machine events in the training set and approximately 70% in new data sets. Neural network techniques are able to adjust to the dynamic mine environment much better than the previous algorithms, consequently, the neural network approach is more acceptable in the applications environment.

Paper Details

Date Published: 20 August 1993
PDF: 10 pages
Proc. SPIE 2059, Sensor Fusion VI, (20 August 1993); doi: 10.1117/12.150268
Show Author Affiliations
Aaron Gordon, Colorado School of Mines (United States)
Hong Chang, Colorado School of Mines (United States)
Robert H. King, Colorado School of Mines (United States)

Published in SPIE Proceedings Vol. 2059:
Sensor Fusion VI
Paul S. Schenker, Editor(s)

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