Real time - anomaly detection/predictive maintenance sample notebooks for energy and manufacturing

David Allan-Oracle

There are some sample notebooks in the repository below for energy and manufacturing. I've kept them very minimalist in the setup needed, so they can be run as is. That makes them easy to try out, get your hands dirty, learn quickly and learn.

The Energy example, processes streaming energy consumption data for:

• Real-time meter monitoring: Continuous tracking of consumption patterns
• Live peak demand detection: Streaming aggregations for demand management
• Anomaly detection: Real-time identification of unusual consumption patterns
• Grid optimization: Continuous data for operational decision-making

https://github.com/oracle-samples/oracle-aidp-samples/blob/main/Notebooks/streaming/energy_delta_streaming_liquid_clustering_demo.ipynb

The Manufacturing example, processes streaming manufacturing data for:

• Real-time equipment monitoring: Continuous tracking of machine performance
• Live quality control: Streaming defect detection and yield analysis
• Production line optimization: Real-time bottleneck identification
• Predictive maintenance triggers: Continuous equipment health assessment

https://github.com/oracle-samples/oracle-aidp-samples/blob/main/Notebooks/streaming/manufacturing_delta_streaming_liquid_clustering_demo.ipynb

Obviously these are demonstration examples but with the ease that you can pick up and learning streaming, analytics and patterns they are very useful. Let me know what you think!

Cheers
David

Brendan T

Great work. Thanks for sharing