492
2.2 Smart connected longwalls
Komatsu has ushered in the age of smart, connected longwall products, complementing them
with advanced analytics to create a digital equivalent of their physical counterparts. The goal
of this is to detect patterns and systematic relationships among the operator inputs, control
system parameters, sensor data and alarm/event information. Statistical techniques, such as
regression analysis, are utilized to optimize cutting profiles, detect roof cavities and improve
roof support cycle times. Predictive diagnostics and rule use cases are implemented to analyze real-time data and provide notifications and alerts of changing longwall characteristics.
The ability to store the longwall system data and correlate it with event trigger points,
enables data analysts to provide effective and efficient feedback to mining operations. State
of the art systems are fundamental for analyzing data from 7,000 plus sensors to determine
critical conditions likely to trigger an event. All of Komatsu’s smart connected products’
information is centrally stored in a cloud storage environment. This enables Komatsu to
schedule and manage data mining around the clock, allowing analysts to collaborate in a
virtual environment to provide customers with solutions as soon as they need them.
Komatsu’s data analytics capabilities drive value into the mining process by turning volumes of sensor data into actionable information used to make real-time decisions to optimize
efficiency, reduce downtime and provide the customer with improved profit margins.
3 MAKING STRATEGIC DATA-DRIVEN DECISIONS
The automated longwall with select user inputs has the ability to operate autonomously with
the shearer cutting coal pass by pass, achieving in excess of 1 million tons per month. Komatsu’s approach to data-driven decisions is unique and provides a significant value add. Some
common questions about this method are:
Which set of smart, connected product capabilities and features should be pursued?
• Komatsu is pursuing extensive product capabilities at multiple levels. Customers can monitor, control, and optimize performance and operation of their equipment. Komatsu is also
in the process of developing a fully autonomous system.
How much functionality should be embedded in the product and how much in the cloud?
• Komatsu chose to build functionality into both the product and the cloud. Certain safety
features are embedded in the machine (for example, emergency shut downs), but others
are managed in the cloud (cavity detection), enabling the equipment to be operated more
safely in challenging underground conditions.
Is this an open or closed system?
• Komatsu employs proprietary, closed systems, reflecting the need for deep design integration involving all the machines in the product system.
Should we develop a full set of smart, connected product capabilities and infrastructure
internally or outsource to vendors and partners?
• Komatsu has chosen to outsource parts of the technology stack while building others inhouse in cases where mining-specific circumstances are critical.
What data must be collected to capture, secure, and maximize the value of its offering?
• Komatsu is capturing data from all our products pertaining to their condition, performance, and service needs. This is necessary to optimize performance, support predictive
maintenance, and enable autonomous operation.
How do we manage ownership and access rights to product data?
• Komatsu gathers and securely manages the data it is collecting from machines in the
cloud. Its ability to ingest this data is part of the value proposition and is well accepted by
customers. The product data is not shared with outside parties. Komatsu has nondisclosure agreements with its customers that allow them to access the machine data. Komatsu
2.2 Smart connected longwalls
Komatsu has ushered in the age of smart, connected longwall products, complementing them
with advanced analytics to create a digital equivalent of their physical counterparts. The goal
of this is to detect patterns and systematic relationships among the operator inputs, control
system parameters, sensor data and alarm/event information. Statistical techniques, such as
regression analysis, are utilized to optimize cutting profiles, detect roof cavities and improve
roof support cycle times. Predictive diagnostics and rule use cases are implemented to analyze real-time data and provide notifications and alerts of changing longwall characteristics.
The ability to store the longwall system data and correlate it with event trigger points,
enables data analysts to provide effective and efficient feedback to mining operations. State
of the art systems are fundamental for analyzing data from 7,000 plus sensors to determine
critical conditions likely to trigger an event. All of Komatsu’s smart connected products’
information is centrally stored in a cloud storage environment. This enables Komatsu to
schedule and manage data mining around the clock, allowing analysts to collaborate in a
virtual environment to provide customers with solutions as soon as they need them.
Komatsu’s data analytics capabilities drive value into the mining process by turning volumes of sensor data into actionable information used to make real-time decisions to optimize
efficiency, reduce downtime and provide the customer with improved profit margins.
3 MAKING STRATEGIC DATA-DRIVEN DECISIONS
The automated longwall with select user inputs has the ability to operate autonomously with
the shearer cutting coal pass by pass, achieving in excess of 1 million tons per month. Komatsu’s approach to data-driven decisions is unique and provides a significant value add. Some
common questions about this method are:
Which set of smart, connected product capabilities and features should be pursued?
• Komatsu is pursuing extensive product capabilities at multiple levels. Customers can monitor, control, and optimize performance and operation of their equipment. Komatsu is also
in the process of developing a fully autonomous system.
How much functionality should be embedded in the product and how much in the cloud?
• Komatsu chose to build functionality into both the product and the cloud. Certain safety
features are embedded in the machine (for example, emergency shut downs), but others
are managed in the cloud (cavity detection), enabling the equipment to be operated more
safely in challenging underground conditions.
Is this an open or closed system?
• Komatsu employs proprietary, closed systems, reflecting the need for deep design integration involving all the machines in the product system.
Should we develop a full set of smart, connected product capabilities and infrastructure
internally or outsource to vendors and partners?
• Komatsu has chosen to outsource parts of the technology stack while building others inhouse in cases where mining-specific circumstances are critical.
What data must be collected to capture, secure, and maximize the value of its offering?
• Komatsu is capturing data from all our products pertaining to their condition, performance, and service needs. This is necessary to optimize performance, support predictive
maintenance, and enable autonomous operation.
How do we manage ownership and access rights to product data?
• Komatsu gathers and securely manages the data it is collecting from machines in the
cloud. Its ability to ingest this data is part of the value proposition and is well accepted by
customers. The product data is not shared with outside parties. Komatsu has nondisclosure agreements with its customers that allow them to access the machine data. Komatsu