Documentation
Get detailed guidance on platform features
Combine the best of Physics with the best of AI for the best condition monitoring from an Intelligent Digital Twin
Our Unique 3 Step Process to Prioritize, Predict, and Propel at Scale
XMPro’s 3-step process in model-based condition monitoring uniquely combines continuous ‘bad actor’ analysis, a hybrid of traditional and AI models for real-time monitoring, and provides ready-to-use blueprints for quick implementation. This structured and tech-enhanced approach, along with accelerated deployment, sets it apart in the predictive maintenance market.
Start your condition monitoring journey by focusing on the most critical elements. XMPro’s Intelligent Digital Twins continually monitor operational assets, providing constant analysis. By identifying and focusing on these critical components, our solution generates practical recommendations, ensuring your condition monitoring efforts are precisely targeted for optimal outcomes.
Experience the power of the XMPro hybrid model-based approach, seamlessly integrating traditional engineering principles with agile AI models. Our approach delivers real-time insights and facilitates proactive measures, safeguarding against potential failures and quality discrepancies.
Expedite your condition monitoring implementation with our ready-to-use blueprints and templates. XMPro offers a comprehensive array of starting blueprints for various asset classes, ensuring a smooth and effective onset to your condition monitoring endeavors.
In order to maximize underground mining operations, the underground conveyor system, a frequent cause of unplanned downtime, needed to reduce its downtime by 30% as an initial target for a predictive maintenance solution
XMPro actively monitors 52 conveyors (spanning over 80+km) in real time, predicting fluid coupling and lagging failures with prescriptive recommendations.
Within five months, the solution identified a potential saving of 184 hours of borer downtime, equating to 44k product tonnes. Exceeding the target, the solution achieved over a 80% reduction in downtime for fluid coupling failures. It now monitors multiple asset types across several mines
"*" indicates required fields