Mining Company Augments SCADA with IoT Platform to Enable Predictive Maintenance

A global mining company was using a SCADA system to gather data points but experienced limited ROI from the solution. Not all industrial assets were connected and most processes were still manual, with expert human intervention required to monitor key machine parameters such as vibration and heat. The customer’s goal was to implement predictive maintenance at the facility, but they quickly realized it would be difficult with the SCADA system since installing and integrating sensors would be expensive and require significant system integration efforts. 

Instead, they sought a solution that could co-exist with the SCADA system for digital transformation. They chose Litmus Edge for its common edge infrastructure and ease of implementation since it could work with the SCADA system but also enable the collection of thousands of data points from any asset, provide analytics and data visualizations, and thus overcome the limitations of the legacy technology in place. 

The mining customer started with a three-month pilot project to collect data directly from the PLCs and machine sensors. Litmus Edge collected and stored tags related to machine running status, alarms, load-cell data and energy meter parameters in a data repository. Real-time alerts were sent to the maintenance team to provide condition-based metrics and identify anomalies and deviations. Basic prediction of any deviation was immediately enabled with the pre-built analytics in Litmus Edge. Over time, as a larger data set was collected during the pilot phase, the customer built a framework for predictive maintenance models. 

Pilot projects are essential to proving out Industrial IoT solutions, and this pilot allowed the customer to successfully validate a key use case: monitoring the condition of machines and enabling predictive maintenance. They found Litmus Edge provided easy connectivity to PLCs, sensors and existing systems at the facility such as SCADA without any disruptions or major system integration efforts. They validated the ability to analyze and monitor machines, then share that data across the business rapidly. 

Upon completion of the pilot, the customer now plans to expand to all areas within the facility to build and deploy predictive maintenance and machine learning models at the edge.