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Estimation and visualization of the truck payload volume and distribution using internet of things, machine learning and augmented reality

University of British Columbia

Managing the payload is key to running safe, efficient and profitable mining operations. Despite the relative simplicity of shovel-truck operations, they are currently not achieving optimum productivity. By overloading trucks, shovel operators can significantly affect the operational efficiency of a mobile fleet and, consequently, reduce the overall profitability of a mining project. Uncertainty around the real volume of the mineral payload hauled from the mine to the processing plant impacts the planning for a smart decision-making process, which increases the risk of operating the mine below a desirable standard of efficiency and profitability. Also, inefficient payload management increases the consumption of fuel and tire, which impacts the carbon footprint by raising demand for elements harmful to the environment. Modern digital and data technologies offer the potential to factor out these drawbacks and to provide digital solutions to optimize shovel-truck performance. This Thesis proposes two approaches for estimating the volume and distribution of the truck payload. The first approach is a batch-performance machine vision system for imaging-based analysis of the payload. The system is developed and tested on a 1/14 model of a mining truck, and results are visualized in an immersive augmented reality environment. The second approach is based on the utilization of a proposed model of machine earning where data from sensors embedded in different parts of the shovel are collected and streamlined to an in-house private cloud for virtualization and processing. A TensorFlow-based ML platform was then used to find correlations between the truck payload and its components for an accurate visualization and volume computation under harsh operating conditions. Finally pros, cons and discussions around its applicability in a real operation were analyzed for both approaches. Analysis of profitability, cost and requirement of computational resources complements the encouraging results attained from both approaches regarding volume computation and visualization of the mining truck payloads.

Text

2020-10-31

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/72016

Mining Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/