Contextualize

Inspiration

I'm always looking to learn and grow through participating in hackathons, and working with industrial data sources was something I had never tried before. There was a lot to take in, so I decided to take some time to focus more on learning about the field and topics to better understand the data I'd be working with.

What it does

This presentation explains some important concepts to know going into contextualizing industrial data sources and documents my learning process.

How I built it

I followed Cognite's instructions use Postman to leverage Cognite API, and for this presentation I ended up using Cognite Asset Data Insight.

Here's a link to some images and .csv file generated that cannot be attached to this project. https://drive.google.com/drive/folders/1xM3or0OP7qZdzVNPrRM9m8DJt6gms2Za?usp=sharing

Challenges I ran into

The biggest challenge I had was to understand the data I was working with. So I took a step back to understand the data more fully.

What I learned

This was a great crash course on mechnical engineering, as I learned so many new concepts and have better insight into the inner workings of an engine and how systems are maintained.

What's next for Contextualize

I'd love to leverage Python SDK to mold the data and present it a way that tells a story and implement some features, including:

• interpolating values (e.g. temperature values) based on historical values when there are data gaps
• adding option to show when significant events (planned or unplanned) happen

Built With

• cognite
• postman