Journal Published Online: 11 May 2022
Volume 6, Issue 1

Identification of Significant Stop Locations in a Mine through GPS Clustering

CODEN: SSMSCY

Abstract

Surface mining applications are highly dynamic environments; conditions one day may not resemble those the next. Not only are these environments subject to changes in weather and other factors, but the locations, personnel, and equipment operations continuously move. Conditions will also vary depending on where equipment and personnel are in the operation. Permanent locations, such as shop and crusher areas, and dynamic locations, such as material loading and dumping areas, will have different lighting, traffic, floor conditions, etc.; personnel need to be aware of these differing conditions to adjust behavior. While modern large-scale mining equipment is generally outfitted with global positioning system (GPS) devices to track machines and support the optimization of operations in the mine, GPS location tracking has also become nearly ubiquitous with the increasing distribution of smart devices. The continued development and implementation of improved data processing and transmission technology increase the potential of equipment to provide more meaningful information to personnel. This paper proposes the hybrid spatial clustering for identification of locations of significance (HSCILS) algorithm—a hybrid of multiple clustering techniques—to analyze GPS data to identify and differentiate different types of locations in the mining operation. The HSCILS algorithm will then be contrasted with the k-means clustering algorithm and the Density-Based Spatial Clustering of Applications with Noise algorithm to demonstrate its capability as an appropriate tool to identify key locations in a mine and differentiate between their nature.

Author Information

Priegnitz, Nathan
Komatsu America Corp., Peoria, IL, USA
Yoo, John
Industrial and Manufacturing Engineering and Technology, Bradley University, Peoria, IL, USA
Pages: 11
Price: Free
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Details
Stock #: SSMS20210038
ISSN: 2520-6478
DOI: 10.1520/SSMS20210038