What's new in Maximo Health 8.8, Maximo Predict 8.8, and Maximo Health and Predict - Utilities 8.6

Unique URL for the Asset details page in Maximo Health

Create a unique URL for the Asset details page to open that page or share with other users or programs. Bookmark the URL and get to the information faster.

Notebook auto upload in Cloud Pak for Data

The auto upload feature uploads all the Maximo Predict notebooks, all data files, the Db2® certificate, the PMI tenant ID, the internal Maximo Predict API URL, and the pmlib.zip. file. For more information, see Automating notebook uploads in IBM Watson Studio.

Factor importance for anomaly detection is shown on the Maximo Predict dashboard

Showing that contributing factors for anomaly scores helps reliability professionals to investigate historical scoring importance. Monitoring how trends change over a selected time range helps detect the cause of anomalies. These insights help users know which assets require service, repair, or replacement.

Factor importance gives users insights into the root cause of anomalous behavior and helps identify the appropriate corrective action to resolve the anomaly and defect. For more information, see Anomaly detection.

More table filters and management options in Maximo Health

Manage columns and filter tables in Scoring and DGA settings and Predict grouping pages.

More granular frequency of data and scoring in Maximo Predict

The asset details dashboard displays hourly grains frequency for failure date, prediction date, and the anomaly detection history score.

Cognos Analytics reporting is available for Maximo Health and Predict - Utilities users

Use the Cognos® reporting capabilities to clean and connect data, create visualizations, and predict outcomes.

You must install Maximo Manage and Cloud Pak for Data before you can use Cognos features because the only ready-to-use integration is in Maximo Manage.

For more information, see Integrating with Cognos Analytics server.

SPSS models can be used for scoring asset conditions in Maximo Predict

Use SPSS® statistical models for training and scoring anomaly detection. Statistical methods are effective in detecting anomalies and provide good cleaning and visualization capabilities.

Maximo Predict supports Cloud Pak for Data 4.6.3 and Python 3.10

Cloud Pak for Data 4.6.3 with Pyton 3.10 is supported in Maximo Application Suite 8.10. For more information, see Upgrading IBM Maximo Predict 8.7 deployment artifacts to 8.8.