Machine Learning Models for Progression Tracking of Impulse Force during High Impact Shovel Loading Operation
Abstract
Large impact force is generated as the large capacity shovel loads 100 tons of material into the dump truck, which in-turn generates high-frequency shockwaves that travels through the truck body, chassis and exposes the operator to whole body vibrations (WBV). Real-time tracking is required for this dynamic impulse force on the truck body which is the sole cause for these vibrations. Therefore, in current work, state-of-the-art machine learning algorithms Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been implemented to track the generation and progression of this dynamic force during a shovel dumping operation. With efficient and realtime tracking, appropriate steps will be taken for minimizing the resulting vibrations and thus improving the operator's health and safety.
Recommended Citation
D. Ali and S. Frimpong, "Machine Learning Models for Progression Tracking of Impulse Force during High Impact Shovel Loading Operation," Proceeding of MineXchange 2020 SME Annual Conference and Expo (2020, Phoenix, AZ), Society for Mining, Metallurgy and Exploration (SME), Jan 2020.
Meeting Name
MineXchange 2020 SME Annual Conference and Expo (2020: Feb 23-26, Phoenix, AZ)
Department(s)
Mining Engineering
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2020 Society for Mining, Metallurgy and Exploration (SME), All rights reserved.
Publication Date
01 Jan 2020
