This document will continue to evolve as existing sections change and new information is added. All updates appear in the following table:
| Date | Product | Feature | Notes |
|---|---|---|---|
| 01 SEP 2020 | Created initial document. |
This guide outlines the information you need to know about new or improved functionality in this update.
DISCLAIMER
The information contained in this document may include statements about Oracle’s product development plans. Many factors can materially affect Oracle’s product development plans and the nature and timing of future product releases. Accordingly, this Information is provided to you solely for information only, is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described remains at the sole discretion of Oracle.
This information may not be incorporated into any contractual agreement with Oracle or its subsidiaries or affiliates. Oracle specifically disclaims any liability with respect to this information. Refer to the Legal Notices and Terms of Use for further information.
Fine-Tune Data Storage and Management
The Data Storage page has been enhanced, so that you can better manage data storage and retention settings for your application.
The new System Metric Data section lets you fine-tune the settings for retaining system metric data. System metrics are inbuilt metrics (KPIs) that are different from the custom metrics that you create in the application.
You can also choose the lifespan for device data, such as application messages, connector messages, and log messages. Sensor data is now included under Training Data.
Incident Details Include Maintenance Work Order Details
If you have machines in Oracle IoT Production Monitoring Cloud Service that are linked to assets in Oracle Maintenance Cloud, and your incident rules are configured to automatically create work orders in Oracle Maintenance Cloud, then you can view the details of the work orders created under the incident details in Operations Center.
The Incidents page in Oracle IoT Production Monitoring Cloud Service includes the IDs of the work orders and their statuses in Oracle Maintenance Cloud.
Factory Settings in Design Center
The Design Center now offers intuitive tiles to view and update factory settings, such as location, floor plans, and maintenance schedule.
You can also select the location in the map instead of providing the location address or coordinates.
Sync Intermediate Bad Quantities with Manufacturing Cloud Service
If bad quantities (reject and scrap quantities) are produced during an intermediate resource instance completion step, they are synced with Manufacturing Cloud Service without waiting for the last resource instance completion step.
Completed quantities are sent only from the last stage of each operation.
New System Metrics for Production Quantities
Factory-level and machine-level metrics are now available to track complete quantities, reject quantities, and scrap quantities at the factory and machine levels. You can add these metrics to your factory-level or machine-level dashboards to view the latest and historical values.
You can also create custom metrics using these system metrics to monitor production quality. You can use these metrics in other analytics artifacts, such as trends, anomalies, and predictions.
You can also configure rules using these metrics as your rule conditions. For example, you may want to trigger an incident if the hourly scrap quantity for a machine exceeds the threshold value.
This document will continue to evolve as existing sections change and new information is added. All updates appear in the following table:
| Date | Product | Feature | Notes |
|---|---|---|---|
| 09 JUL 2020 | Created initial document. |
This guide outlines the information you need to know about new or improved functionality in this update.
DISCLAIMER
The information contained in this document may include statements about Oracle’s product development plans. Many factors can materially affect Oracle’s product development plans and the nature and timing of future product releases. Accordingly, this Information is provided to you solely for information only, is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described remains at the sole discretion of Oracle.
This information may not be incorporated into any contractual agreement with Oracle or its subsidiaries or affiliates. Oracle specifically disclaims any liability with respect to this information. Refer to the Legal Notices and Terms of Use for further information.
Use the new MQTT server connector in Oracle IoT Production Monitoring Cloud Service to enable direct MQTT connectivity from devices and network providers. The MQTT server connector removes the need for an intermediate gateway or external MQTT broker.
The connector uses the specified target factory to import the machines and machine types in Oracle IoT Production Monitoring Cloud Service. You can monitor the sensor data in operations center.
Export and Import Organizations
As an administrator, you can export, or back up, an organization in Oracle IoT Production Monitoring Cloud Service. The organization is exported along with all its entities, such as factories, machine types, machines, associated rules, anomalies, predictions, settings, and integrations. You can then import the exported archive file into another instance of Oracle IoT Production Monitoring Cloud Service.
For example, you can export an organization to create a backup before an update. You can also export an organization to move the organization from a test environment to a production environment once the tests are complete.
PMML Data Engineering Support for Predictions
If you wish to use a pre-trained prediction model in place of the automatic prediction training in Oracle IoT Production Monitoring Cloud Service, you can upload the trained model in Oracle IoT Production Monitoring Cloud Service to create a prediction. Oracle IoT Production Monitoring Cloud Service then performs the prediction scoring using your pre-trained model.
You can use training models supported by pmml4s (PMML Scoring Library for Scala), such as the neural network. When creating a new prediction, upload your PMML file to replace the built-in models used by Oracle IoT Production Monitoring Cloud Service.