Example
Warning: Use of undefined constant WC_Product - assumed 'WC_Product' (this will throw an Error in a future version of PHP) in /var/www/html/wp-content/themes/digital-twin-exchange/functions.php on line 490Example or demonstration product to allow users to try out the Digital Twin Exchange using realistic example products and data.
Showing all 4 results
-
AI model to detect Personal Protection Equipment for worker safety
0.00 credit(s)AI model to detect helmets, vests, gloves and glasses to ensure safety of workers.
Download limit: unlimitedDownload availability: no expirationAI model to detect helmets, vests, gloves and glasses to ensure safety of
Add to cart -
Analyze sedimentation tanks for cleanliness
0.00 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 -
Computer Vision model to classify assets on Transmission towers
0.00 credit(s)Engage a prebuilt deep learning model to analyze defective assets on a transmission tower. The model trained on […]
Download limit: unlimitedDownload availability: no expirationEngage a prebuilt deep learning model to analyze defective assets on a transmission tower The model trained on IBM Maximo Visual Inspection can identify 25 different assets on a transmission tower Enterprises are transforming businesses by leveraging AI methods to quickly analyze health of remote assets for performance and maintenance Explore IBM Maximo suite of offerings, where complete solution from a video footage from drones can be monitored for ensuring no loss in productivity
Add to cart -
Failure Models: Predict equipment failure using IoT sensor data
0.00 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