IBM Maximo Predict
IBM® Maximo® Predict is an application in IBM Maximo Application Suite that you can use to improve your assets' and locations' reliability and for assets, predict downtime, degradation, and failures.
What is Maximo Predict?
By using Maximo Predict, you can use artificial intelligence (AI) and your performance data, maintenance records, inspection reports, and environmental data to predict downtime, degradation, and failures to track imminent failures and maintenance schedules. Maximo Predict includes all of the features that are available in Maximo Health and the features that are described in the following sections.
For more information about how to deploy the application, see Deploying and activating Maximo Predict.
For more information about how to get started with the application, see Getting started.
Predict your assets' futures
You can create groups of assets and then work with your data scientist to generate different types of predictions for those assets, such as current failure probability or failure dates. Your data scientist can use the group ID and the default notebooks to build and train instances of a predictive model. After a model is trained and deployed, for each asset, a Predictions section is populated with predictions and related data for the asset. For example, asset records might contain the current probability of different failure modes, the number of days until a specific failure mode might occur, or if detected anomalies signal a probable failure. You can also detect operating context-specific anomalies. For more information about the default notebooks, see Default notebooks.
Additionally, your data scientist can configure custom notebooks that are either extensions of default notebooks or completely custom, but the models must be deployed in Watson™ Machine Learning.
Track imminent failures and maintenance schedules
Use work queues to track assets that have a high probability of failure or assets that will fail before the next scheduled generation of a preventive maintenance work order.