Using Telemetry to Assess Operator Effects on Hydraulic Shovel Energy Efficiency. Part I: Algorithms for Extracting Explanatory Variables from Sensor Data

Abstract

Hydraulic shovels are essential and dominant equipment in mining because of their efficiency, flexibility, and productivity. Technological advancements in these machines have focused on improving their productivity and energy efficiency because of the drive to curb their energy consumption and carbon emissions. However, the effect of operators' practices on their energy use and productivity is a critical factor that is frequently ignored. Increasing energy efficiency in these areas is vital since the mining sector accounts for significant energy consumption and associated carbon emissions. Although it has been acknowledged in earlier studies that operator practices affect the effectiveness of loading machines, research has sparingly measured this impact empirically in the case of hydraulic shovels. The originality of this work is from the fact that it fills this gap by utilizing telemetry and statistical data analysis to look at how different operator practices affect the energy use and productivity of hydraulic shovels. This paper is the first of a two-part series that uses telemetry to develop an approach to empirically evaluate the effect of operators' practices on hydraulic shovels. This paper presents algorithms developed for extracting explanatory variables from sensor data that can be used to carry out this empirical study effectively. The work shows that the developed algorithm for sampling cycles from the sensor data is 98% accurate. Algorithms based on the cycle sampling algorithm successfully extract cycle time components, dump height, payload, boom energy, boom, bucket, stick, and swing angles from the sensor data.

Department(s)

Mining Engineering

Keywords and Phrases

Algorithms; Energy efficiency; Hydraulic shovels; Operator practices; Sensor Data; Telemetry

International Standard Serial Number (ISSN)

2524-3470; 2524-3462

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Springer, All rights reserved.

Publication Date

01 Jan 2024

Link to Full Text

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