Predicting Equipment Failure in Canadian Mines

In order to reach the production targets, mining operations typically need to operate without a stop. Equipment maintenance planning plays an important role in ensuring the required number of mining haul trucks are available at any given time. If not planned correctly, corrective maintenance would be required, which could slow down or halt the production as well as potentially costing higher in maintenance. In this project, reliability analysis and predictive maintenance for turbocharger and transmission failures will be proposed using physics-based, data-based, machine learning based and hybrid approaches, using the data collected by the sensors on mining haul trucks. The factors that increase the fuel consumption will also be studied. Finally, anomaly detection model will be developed to detect sudden sensor reading changes and failures for the cases to alert the driver or an operator.

Faculty Supervisor:

Yuksel Asli Sari

Student:

Partner:

Symboticware

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

Queen's University

Program: