Software: Forecasting and Dashboarding for Predictive Maintenance
What?
Development of an AI/ML application that will predict the moment of degradation/failure of an aircraft gas turbine engine well in advance based on historical data emanating from multiple sensors.
How?
By developing machine learning strategies in order to effectively build a machine learning model involving multiple time-series data. The following key technologies were used to achieve the objectives:
- Streamlit for UI/Dashboard
- Heroku cloud for app deployment
- Python for the development of predictive maintenance app
- Explainable AI technologies to boost confidence and trust
- DeepAR Neural Networks for building time-series models
- Probabilistic Forecasts to make decisions with confidence intervals
- MxNet & GluonTs frameworks for experimenting/building machine learning models
- Azure Blob Data Storage for maintaining hundreds of time-series sensor data & AI artifacts
- Docker for application isolation towards deployment
- Gitlab CI/CD for continuous integration & continuous deployment
May I see it ?
The following video shows a minimal version of the predictive maintenance dashboard.