Date on Master's Thesis/Doctoral Dissertation
Computer Engineering and Computer Science
Computer Science, MS
Committee Co-Chair (if applicable)
data mining; predictive maintenance; mining trucks; sequential pattern mining
The mining industry is one of the biggest industries in need of a large budget, and current changes in global economic challenges force the industry to reduce its production expenses. One of the biggest expenditures is maintenance. Thanks to the data mining techniques, available historical records of machines’ alarms and signals might be used to predict machine failures. This is crucial because repairing machines after failures is not as efficient as utilizing predictive maintenance. In this case study, the reasons for failures seem to be related to the order of signals or alarms, called events, which come from trucks. The trucks ran twenty-four hours a day, seven days a week, and drivers worked twelve-hour shifts during a nine-month period. Sequential pattern mining was implemented as a data mining methodology to discover which failures might be connected to groups of events, and SQL was used for analyzing the data. According to results, there are several sequential patterns in alarms and signals before machine breakdowns occur. Furthermore, the results are shown differently depending on shifts’ sizes. Before breakdowns occur in the last five shifts a hundred percent detection rates are observed. However, in the last three shifts it is observed less than a hundred-percentage detection rate.
Kahraman, Abdulgani, "Maintainability analysis of mining trucks with data analytics." (2018). Electronic Theses and Dissertations. Paper 2932.