Unlocking Predictive Maintenance Capabilities: Exploring IBM MAS Predict

As we continue our exploration of the IBM Maximo Application Suite (MAS), our next application in focus is IBM MAS Predict - the crown jewel of MAS and final step in your journey towards predictive maintenance. Predict uses advanced Artificial Intelligence (AI) and Machine Learning (ML) models to forecast asset failures with precision. 

By combining failure history and asset information from MAS Manage with real-time IoT sensor data from MAS Monitor, Predict provides actionable insights to anticipate and prevent failures. Predict’s forecasts are displayed alongside health scores from MAS Health, creating a unified interface for assessing asset performance and risks.

The Role of MAS Predict in Predictive Maintenance 

MAS Predict is designed to transform maintenance operations by shifting from interval based preventive maintenance to a more data driven health based predictive maintenance. Its ML models analyze vast amounts of current and historical data, uncovering patterns that indicate potential failures. Predict delivers insights directly within the Maximo Health application, offering maintenance teams a seamless view of an asset’s health and predictive insights. 

Predict provides following predictive models out of the box, custom models can be created if needed, 

• Predicted failure date to forecast when assets are likely to fail. 

• Probability of failure to provide likelihood of assets to fail within a given period. 

• Anomaly detection to highlight deviations from normal behavior. 

• End-of-life curves to estimate the remaining useful life of assets. 

These pre-built models enable organizations to deploy Predict quickly and effectively, unlocking immediate value. 

Seamless Integration with Other MAS Applications 

A hallmark of the MAS ecosystem is its ability to deliver an integrated solution across all applications. Predict seamlessly integrates with:  

• Maximo Manage: Installation date and failure dates are sent to Predict from Manage. The integration ensures that all failure history and asset’s age are considered, leading to more accurate predictions. 

• Maximo Monitor: By harnessing IoT sensor data from Monitor, Predict enhances its forecasts with real-time operational metrics, such as temperature, pressure, and vibration trends. This integration provides a complete view of asset’s condition and performance. 

• Maximo Health: Predict’s insights are displayed alongside health scores from Maximo Health application. This unified interface ensures that maintenance teams have all the critical information they need in one place, driving better decision-making. For example, when a failure probability forecast from Predict is paired with a deteriorating health score from Maximo Health, maintenance teams gain the confidence to act swiftly, optimizing both asset performance and resource allocation, avoiding a costly unplanned breakdown. On the other hand, if an asset’s health scores are good and failure probability is low, a scheduled maintenance may be delayed, leading to increased uptime and cost savings.  

With that I will signoff for now, stay tuned for more insights into the innovative features and functionalities of the Maximo Application Suite!