Asset management and reliability are of critical importance in the mining industry, especially surrounding employee safety and avoiding the high cost of downtime. The barrier to world class maintenance, however, is that most equipment failures are random and difficult to detect before they happen. In fact, it is estimated that most failures fall in the random category.
Gathering equipment data in the mining industry has historically presented challenges due to the siloed and distributed nature of the production process. Data sources are located across the facility and mines with no clear unified network connecting them back to a main hub. This results in fragmented visibility and manual data workflows. Information technology (IT) and operational technology (OT) exist independently, and the available data often lacks context, making it difficult to understand and use. This lack of connectivity limits the ability to pull performance metrics from specific industrial equipment.
Lack of connectivity and access to data also make it difficult to implement enterprise-wide predictive maintenance solutions. One off and proof-of-concept on just one piece of equipment or a very small, networked group doesn’t make for a reliable predictive maintenance solution, and still leaves much of maintenance to chance.
The need for meaningful improvement
It’s clear that maintenance should move away from being reactive to improve efficiencies, but getting there will require an investment in a more comprehensive maintenance infrastructure. Simply scheduling maintenance on a regular rotation doesn’t consider the actual condition of the equipment.
To manage assets effectively a mining company must understand why, how and when they are failing. Maintenance as a profession and as a corporate practice has evolved substantially over the last 50 years and technology has evolved alongside. However, the barriers to adoption of technology that can offer predictive maintenance in the mining industry tie back to cost and the spread-out nature of operations; there is no one-size-fits all option.
What’s needed is a solution that can provide data contextualization at scale and solve the data architecture and integration challenges that currently inhibit scaled deployments of predictive maintenance solutions. This is where industrial data operations systems come into play.
Industrial Data Operations
Industrial Data Operations (DataOps) supports the development of a predictive maintenance solution for mining. The technologies used in DataOps focus on data science to develop a predictive model. It can automate data collection and collation, something that used to be a time-consuming manual process. By bringing together OT and IT through a system like Asset Intelligence for Mining, built on the Rockwell Automation FactoryTalk® DataMosaix™ platform, predictive maintenance becomes feasible.
Data scientists will now have more time available to read the data and recognize trends in machine health and performance. This insight will then allow them to determine the right maintenance cadence for each piece of equipment and they are able to make that information available to relevant personnel across the operation on easy-to-read dashboards on tablets, laptops or desktops. Valuable, near real-time information can reach the right person before a piece of equipment breaks down causing a costly shutdown.
Industrial DataOps adds and manages meaningful relationships between previously disparate data, accelerating the development and deployment of machine learning applications like predictive maintenance. To put it in another way, it simplifies how a mining company can extract value from complex industrial data.
Putting it all together
Combining real-time sensor data with asset models that are based on first principles of engineering (the idea that you can build complex stuff from simpler stuff) provides pre-built models underpinning much of how Asset Intelligence for Mining operates. DataOps operationalizes those models.
Predictive maintenance can reduce personnel hours spent on maintenance, reduce unscheduled downtime and increase productivity.
Learn more about Industrial DataOps and the Rockwell Automation Industry Solution – Asset Intelligence for Mining.
