Mining Goes Digital – Mueller et al. (Eds)
© 2019 Taylor & Francis Group, London, ISBN 978-0-367-33604-2
491
The digital mine eco-system
Willem A.S. Fourie
Information, Communication and Technology, Komatsu Mining Corporation, Warrendale,
Pennsylvania, USA
ABSTRACT: Komatsu’s business focus is to provide surface and underground mining
operations with superior equipment, solutions and direct service. The goal is to deliver the
lowest cost per unit of production over the life cycle of the equipment while prioritizing
safety. To achieve these results, Komatsu utilizes data, prognostics and analytics in applications throughout Smart Solutions centers around the globe to improve production, refine
operations, detect pre-mature failures and implement preventative strategies. The smart
connected products of a Joy longwall system include a shearing machine, Armored Face
Conveyor (AFC), roof supports, stage loader, crusher, and mobile belt tailpiece. While monitoring longwall operations, the ongoing aim is to detect patterns and systematic relationships amongst the operator inputs, control system parameters, sensor data and alarm/event
information. Statistical techniques, such as regression analysis, and single and multi-variables
analysis are utilized to optimize cutting speed, detect roof cavities and improve roof support
cycle times. Predictive diagnostics and analytic rules are implemented to analyze real-time
data and provide notifications and alerts of changing longwall characteristics.
Keywords: JOY, RS20s, FaceBoss, Longwall systems, Shearers, Analytics
1 INTRODUCTION
Over the past several years, we have seen mining companies use machine learning and deep
learning to support longwall mining. The internet, big data and improvements in processing
power have allowed the mining industry to measure, monitor and analyze more information
than ever before. To help customers make the most of this new technology, Komatsu has
pioneered processes to capture machine data and transform it into knowledge that allows
customers to make the best decisions possible.
This paper will discuss how digital technologies allow mines to transition from a reactive, unit-based monitoring approach, to a proactive method that utilizes modern technologies. System and performance monitoring enable users to link various data sources including
operator performance, maintenance, logistics and safety systems.
2 SMART CONNECTED PRODUCTS
2.1 Longwall mining
A longwall system consists of a shearer, powered roof support system, armored face conveyor, belt conveyor, power supply and pump stations all of which are linked together. As the
longwall system moves forward in the mine, it extracts an average of 2,200 tons of material
per hour, filling a 100-ton railcar in less than three minutes. A Joy automated longwall system
monitors and controls important aspects of the system such as roof support leg pressures,
electric motor currents, voltage speeds, temperature, vibration, and many other things. It also
transmits data between individual pieces of equipment, as well as up to the surface.
© 2019 Taylor & Francis Group, London, ISBN 978-0-367-33604-2
491
The digital mine eco-system
Willem A.S. Fourie
Information, Communication and Technology, Komatsu Mining Corporation, Warrendale,
Pennsylvania, USA
ABSTRACT: Komatsu’s business focus is to provide surface and underground mining
operations with superior equipment, solutions and direct service. The goal is to deliver the
lowest cost per unit of production over the life cycle of the equipment while prioritizing
safety. To achieve these results, Komatsu utilizes data, prognostics and analytics in applications throughout Smart Solutions centers around the globe to improve production, refine
operations, detect pre-mature failures and implement preventative strategies. The smart
connected products of a Joy longwall system include a shearing machine, Armored Face
Conveyor (AFC), roof supports, stage loader, crusher, and mobile belt tailpiece. While monitoring longwall operations, the ongoing aim is to detect patterns and systematic relationships amongst the operator inputs, control system parameters, sensor data and alarm/event
information. Statistical techniques, such as regression analysis, and single and multi-variables
analysis are utilized to optimize cutting speed, detect roof cavities and improve roof support
cycle times. Predictive diagnostics and analytic rules are implemented to analyze real-time
data and provide notifications and alerts of changing longwall characteristics.
Keywords: JOY, RS20s, FaceBoss, Longwall systems, Shearers, Analytics
1 INTRODUCTION
Over the past several years, we have seen mining companies use machine learning and deep
learning to support longwall mining. The internet, big data and improvements in processing
power have allowed the mining industry to measure, monitor and analyze more information
than ever before. To help customers make the most of this new technology, Komatsu has
pioneered processes to capture machine data and transform it into knowledge that allows
customers to make the best decisions possible.
This paper will discuss how digital technologies allow mines to transition from a reactive, unit-based monitoring approach, to a proactive method that utilizes modern technologies. System and performance monitoring enable users to link various data sources including
operator performance, maintenance, logistics and safety systems.
2 SMART CONNECTED PRODUCTS
2.1 Longwall mining
A longwall system consists of a shearer, powered roof support system, armored face conveyor, belt conveyor, power supply and pump stations all of which are linked together. As the
longwall system moves forward in the mine, it extracts an average of 2,200 tons of material
per hour, filling a 100-ton railcar in less than three minutes. A Joy automated longwall system
monitors and controls important aspects of the system such as roof support leg pressures,
electric motor currents, voltage speeds, temperature, vibration, and many other things. It also
transmits data between individual pieces of equipment, as well as up to the surface.