As more data is collected from your mobile fleet for real time monitoring and data analytics, the pressure placed on your mine’s wireless network has never been so high. Often associated with the collection of this data is an increase in hardware installed on-board your mobile fleet for all the required applications, as well as the connectivity solution.
With an increasing number of mines in the process of digital transformation, reliability of your wireless network is becoming paramount. Real time or near real-time data access to fleet data is now a requirement for mines of all sizes, and failure of those wireless networks isn’t only critical for autonomous sites.
We will discuss here five examples of how network downtime, even in a non-autonomous environment, can affect your productivity by impacting a variety of business units within your mine.
While the large mining houses move rapidly towards automation of mobile operations, generally their whole operational environment is powered by a single OEM chosen by the customer. This strategy not only specifies the types of trucks a miner uses for example, but also dictates the technologies required to support the autonomous operations, from the wireless infrastructure to the machine access technology. All of which has to be approved by the machine autonomy vendor.
If a customer wishes to use alternate technologies, the costs and logistics associated to validate and test a customer preferred alternative are often prohibitive and so, in most cases, things stay the same and the advantage to the customer of implementing newer and better performing solutions is lost.
Deployment of an out-of-the-box fleet management or asset health solution is not always an option for smaller operators. Add the cost of the application to the cost of deploying a full scale wireless network and many are unable to justify the ROI on their project, and remain stuck in the manual collection of their data.
The applications traditionally have requirements for relatively high cost hardware and software, as well as a network requirement for full coverage throughout the site. That doesn’t make it the only solution.
Most mines now have the ability to access some of their data in some ways, whether be in real-time, near real-time or manually. However, what the industry now calls the ‘Digital Mine’, defined by the installation of a variety of applications on-board the heavy mobile equipment and real-time access to the data they generate, often remains an option only available to larger operators.
Even for those operations, reliable real-time access to the machines’ critical data can remain a challenge caused both by a limitation in availability of access to all the data and an excessive amount of data travelling through the mine’s wireless network at any one time.
Access to the mobile equipment’s data can be available to all. We will discuss here some of the main challenges faced with data access and ways to address them.
Increase of safety, productivity and decrease of overall machine downtime have been key drivers for digitalization in mining. This process has however a cost that may have limited or slowed down some miners in deploying technologies at their site, due to a challenge in calculating and predicting ROI.
The fact is that without a proper long term plan of your data requirements, your technology expenditure can be significantly higher than expected over the course of several years. An open computing platform can create a bridge for deployment of multiple technologies over a period of time, supporting migration to a Digital Mine cost effectively.
Data is the core of the digital mine. One of my first projects in mining was catching haul trucks during shift changes, at fuel islands, and in the truck shops. I would download the asset health data from the machine onto a laptop, then take that data back into the office, and import it into our database. I’d spend as much time as I could with the mechanics in the shop, the maintenance planners, and the dispatchers, trying to understand challenges and listen to stories of frustration that seemed to be frequently repeated. I’d take those stories back to the database and try to find the data that would help us understand those challenges, and any triggers or tales that may be told by the data.