Asset Monitor Robotic Twin Simulator and Dashboard
0 credit(s)
This digital twin provides device, data, and dashboard downloads that demonstrate how an IoT administrator can define simulated devices, apply analytics, build dashboards, and view the status and metrics in the dashboards.
The complete tutorial is part of Monitoring End to End Tutorial at: https://www.ibm.com/support/knowledgecenter/SSQP8H/iot/dashboard/tutorials/dashboard_tutorials.html
Description
In this 4 part tutorial you will learn to:
Part 1: Add data
Create a painting robot entity type and use REST APIS to send device events.
Part 2: Add dimensions and image
Add dimensions and descriptive images to the your entities (devices)
Part 3: Add an instance dashboard
Configure an instance dashboard for the robot entity type that you created. Monitor the load levels for a specific painting robot.
Part 4: Add a summary dashboard
Configure a summary dashboard for the robot entity type that you created. Monitor the average work completed by all of the painting robots in the automotive production line.
Additional information
| Digital Twin Resources |
|---|
Only logged in customers who have purchased this product may leave a review.
Refund Policy
- not applicable -
Cancellation / Return / Exchange Policy
Product is offered as a free, non-supported example of an AI model and dashboard resources packaged for Maximo Asset Monitor. Please submit any questions as a direct message to AI Applications Store, or use site resources for help.
End User License Agreement
General Inquiries
There are no inquiries yet.
Related Digital Twins
Related products
-
Isolation Forest Anomaly Detection Model
0 credit(s)The Isolation Forest Anomaly Detection Model for IBM® Maximo® Asset Monitor applies the open source model and includes […]
Download limit: unlimitedDownload availability: 365 daysThe Isolation Forest Anomaly Detection Model for IBM® Maximo® Asset Monitor applies the open source model and includes specialized IoT models, analysis notebooks, functions, and dashboards for Anomaly
Add to cart -
Failure Models: Predict equipment failure using IoT sensor data
0 credit(s)Walk through a prediction methodology that utilizes multivariate IoT sensor data to predict equipment failure. Accessing this product […]
Download limit: unlimitedDownload availability: 365 daysWalk through a prediction methodology that utilizes multivariate IoT sensor data to predict equipment failure Accessing this product will take you to the IBM Data Model at https://developeribmcom/patterns/predict-equipment-failure-using-iot-sensor-data/ By adding to cart or accessing this content, you agree to the terms of use for the
Add to cart -
Analyze sedimentation tanks for cleanliness
0 credit(s)Deep learning model to detect dirty and clean sedimentation/setting tanks at water treatment facilities.
Download limit: unlimitedDownload availability: no expirationDeep learning model to detect dirty and clean sedimentation/setting tanks at water treatment
Add to cart
Reviews
There are no reviews yet.