Optimization of Quarry Operations and Maintenance Schedules

New technologies such as the Internet of Things are providing newer insights into the health, performance, and utilization of mining equipment through the collection of real-time data with sensors. In this study, data is utilized from multiple quarries and a surface coal mine collected through the software CAT Productivity and CAT MineStar Edge to analyze the performance of loaders and haul trucks. This data consists of performance metrics such as truck and loader cycle time, payload per loader bucket, total truck payload, truck plan distance, and loader dipper count. This study uses data analysis and machine learning techniques to analyze the performance of loaders and haul trucks in the mining operations used in the scope of this study. Data analysis of cycle time and payload show promising results such that there is an optimum cycle time for multiple loaders between 30-40 seconds that show a high average production. Furthermore, the distribution of production variables is analyzed across each set of loaders to compare the performance. The Caterpillar 992K machine in the rock quarries data set seemed to be the highest-yielding machine while the two Caterpillar 993K machines performed similarly in the surface coal mine data set. The Neural Network algorithm created a model that predicted the loader from the performance metrics with 90.26% accuracy using the CAT Productivity data set, while the Random Forest algorithm achieved a 79.82% accuracy using the CAT MineStar Edge data set. Furthermore, the use of preventative maintenance is investigated in the process of replacing Ground Engaging Tools on loader buckets to determine if maintenance was effective. Additionally, data analysis is applied to Ground Engagement Tools maintenance to identify key preventative maintenance schedules to minimize production impact from equipment downtime and unnecessary maintenance. Production efficiency is compared before and after maintenance on Ground Engaging Tools and concluded that there was no material change in the average production of the mine based on that analysis. The insights gained from this study can inform future research and decision-making and improve operational efficiency.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Mining Engineering

Department dc:contributor.department

Mining Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2023

Author dc:creator

• George, Brennan Kelly

Chair dc:contributor.committeechair

• Nojabaei, Bahareh

Committee members dc:contributor.committeemember

• Barros Daza, Manuel Julian
• Westman, Erik Christian

• Mining Industry
• Big Data
• Optimization
• Maintenance
• Machine Learning

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:37849

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/115571