Paper
20 August 1993 Neural networks for processing data from multiple redundant sensors for mine systems management, operation, maintenance, and control
Aaron Gordon, Hong Chang, Robert H. King
Author Affiliations +
Proceedings Volume 2059, Sensor Fusion VI; (1993) https://doi.org/10.1117/12.150268
Event: Optical Tools for Manufacturing and Advanced Automation, 1993, Boston, MA, United States
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.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Aaron Gordon, Hong Chang, and Robert H. King "Neural networks for processing data from multiple redundant sensors for mine systems management, operation, maintenance, and control", Proc. SPIE 2059, Sensor Fusion VI, (20 August 1993); https://doi.org/10.1117/12.150268
Lens.org Logo
CITATIONS
Cited by 1 scholarly publication.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Neural networks

Mining

Sensor fusion

Sensors

Land mines

Signal processing

Algorithm development

Back to Top