Proof-of-Concept Dashboard for future work on this area
User Actions: User is expected to perform these actions
App functions: Once User Action is met, app functions will be activated
** Not all requires User Action
Upload Data: Upload predictive maintenance data for visualization & model training purposes
User Actions
- Upload data using Browse files option
- Select TTF, event variable & categorical variables using the drop-down options
- Click Confirm Variables selected to process the data and variables
App Functions
- Uploaded data will be saved locally for visualization
- App will process the data based on given inputs
Data Visualization: Pre-defined charts are used to visualize the data
App Functions
- Histogram plots on numerical dataset
- Correlation plot
- Kaplan Meier plot
Model Training : Train your model using defined parameters.
User Actions
- Dropdown options for model type to train on
- Train Test Split ratio using slider
- Lastly click Begin Model Training to train your model
App functions
- Model begins training and stores the artifact locally
Model Evaluation: Evaluate the model using metrics. Optimal Threshold will be generated
App functions
- Shows Model results e.g. C-index, Brier Score
- Obtain the optimal threshold for each model
Model Inference: Used to get predictions using a trained model
User actions
- Select the trained model to infer
- Upload data for predictive maintenance
- Select the appropriate threshold and click Begin Inference
App Functions
- Survival Curves and Hazard Rates for each asset will be generated
- Time to failure will be generated for each asset
- Python
- Streamlit
- PySurvival Package
- Plotly