Machine Learning and AI Application for Shovel Loading Operation
Developing State-of-the-art AI & Deep Learning Framework for Real-time Monitoring to Optimize Operations
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Shovel-truck systems are the most widely employed excavation and material handling systems for surface mining operations. During this process, a high-impact shovel loading operation (HISLO) produces large forces that cause extreme whole body vibrations (WBV) that can severely affect the safety and health of haul truck operators. The research for solution development of this HISLO problem involved the application of artificial intelligence and machine learning for implementing the impact force detection in real time for a multi-pass shovel loading operation. A detailed step-by-step procedure in...
Shovel-truck systems are the most widely employed excavation and material handling systems for surface mining operations. During this process, a high-impact shovel loading operation (HISLO) produces large forces that cause extreme whole body vibrations (WBV) that can severely affect the safety and health of haul truck operators. The research for solution development of this HISLO problem involved the application of artificial intelligence and machine learning for implementing the impact force detection in real time for a multi-pass shovel loading operation. A detailed step-by-step procedure involved in the successful application of machine learning, deep learning, and artificial intelligence for this real-world problem has been provided in this book.This work was a pioneering effort for advancing knowledge and frontiers in addressing the WBV challenges in deploying heavy mining machinery in safe and healthy large surface mining environments. The smart and intelligent real-time monitoring system minimizes the impact force on truck surface, which in turn reduces the level of vibration on the operator, thus leading to a safer and healthier working mining environment.
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