The many facets of PdM: Mining company transitions from manual monitoring to automation
The medley of predictive maintenance (PdM) strategies for improving machine health is growing larger and more powerful, whether using classic portable tools for non-critical asset inspection rounds and on-site problem verification and troubleshooting, or advanced technologies such as the IIoT, cloud, and AI and ML algorithms.
Leaders and analysts who go on record by documenting improvements gained from predictive maintenance initiatives provide a window into the immense potential of today’s enabling technologies. This article is one of seven diverse case studies that illustrate some of the many PdM methods and applications employed today.
The other case studies include:
- Analyst foresees AI/ML driving widespread adoption of prescriptive maintenance
- Oil and gas supermajor uses AI predictive analytics
- Midstream energy company uses IIoT strategy with integrated CMMS
- Consumer products manufacturer uses AI and ML models
- Self-driving truck company uses CMMS, BI tooling, and mobile app
- Tire manufacturer uses 24/7 wireless vibration monitoring system
- Thermal battery manufacturer uses Generative AI-driven data operations platform
Challenge: A large mining facility’s SCADA system was collecting machine data but not yielding deep device intelligence. Manual processes were still prevalent, including expert intervention to monitor parameters such as vibration and heat. To increase operational efficiency and facilitate digital transformation, an IIoT platform that could co-exist with the SCADA system to enable predictive maintenance was needed.
Solution: Litmus Edge from Litmus Automation was chosen because of its ability to work with the SCADA system and support the collection of thousands of data points from any asset. Deploying the solution on the mining company’s gateway devices provided a common infrastructure for delivering instant analytics and data visualizations as well as real-time alerts to condition anomalies and deviations for predictive maintenance.
Results: Starting with a three-month pilot, Litmus Edge collected and stored data directly from PLCs and machine sensors. Pre-built analytics immediately enabled basic prediction of any asset condition deviation, and real-time alerts were delivered to the maintenance team for corrective action. With machine learning, as a larger data set was collected and shared, the customer built a framework for predictive maintenance models. Having validated a key use case, the company now plans to extend the solution to all areas within the facility.
