Masters Theses
Abstract
"Modern mining machinery is equipped with large number of sensors that monitor its condition and performance. Voluminous data collected through those sensors helps with failure diagnostics, warns operators of impending failures, and can be used to assess mine performance. Availability of large volumes of this data, gathered in real time and transmitted over wireless mine communication systems, together with availability of sophisticated data processing tools and the related hardware, provide a number of opportunities for further enhancement of mine performance. One such approach is application of data mining techniques for knowledge discovery in the databases containing condition and performance records of mining equipment. The research describes the study undertaken to explore one such opportunity.
Caterpillar’s VIMS system provided the necessary data, collected at Jwaneng Mine in Botswana. IBM’s Intelligent Miner was utilized as the data-mining tool. Both statistical and data mining functions of Intelligent Miner were used to analyze the data for previously unknown relations or patterns that may have effect on the condition and performance of mine trucks. One of the major tasks of this research was to validate compatibility of data source with the data-mining tool.
The study has shown that data mining is a useful tool for knowledge discovery in mining equipment databases. In particular it allows defining and quantifying relations between various parameters that define condition and performance of mine trucks. Its results form the basis for further investigations that may lead to a development of predictive capability in assessing condition and performance of mining equipment"-- Abstract, p. iii
Advisor(s)
Golosinski, Tad S.
Committee Member(s)
Bullock, Richard Lee, 1929-
Erçal, Fikret
Department(s)
Mining Engineering
Degree Name
M.S. in Mining Engineering
Publisher
University of Missouri--Rolla
Publication Date
Summer 2001
Pagination
xi, 95 pages, CD-ROM
Note about bibliography
Includes bibliographical references (pages 91-94)
Rights
© 2001 Ibrahim Kaan Ataman, All rights reserved.
Document Type
Thesis - Restricted Access
File Type
text
Language
English
Thesis Number
T 7921
Print OCLC #
47775875
Electronic OCLC #
1484839036
Recommended Citation
Ataman, Ibrahim Kaan, "Data mining for prediction of condition and performance of mine machinery" (2001). Masters Theses. 2058.
https://scholarsmine.mst.edu/masters_theses/2058
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