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Predictive Maintenance and Diagnostics for Mining Equipment

Authors: [tex2html_wrap4436]P. Knights, L.K. Daneshmend

Investigator username: laeeque

Category: expert systems

Subcategory:

Mining equipment operates in an extremely harsh environment, with severe penalties for operational failure. This project focuses on the formulation and integration of front-end signal interpretation and high-level decision-support for maintenance of underground mobile mining equipment. Such equipment is unique and challenging in many ways, e.g. duty cycles, loads, terrain, and operator practices are all extremely variable. A specific piece of mining equipment has been chosen as the focus of this work: an underground load-haul-dump (LHD) vehicle. Conventional signal-processing as well as neural-network techniques are being investigated for fault characterization and detection. Knowledge-based decision-support techniques are being developed for formulating on-line advice to maintenance personnel.


baron@cim.mcgill.ca