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Data Collection for Fault Cases
- Completed power fault synthetic test patterns
- Started reduced flow synthetic test patterns data collection loop
Regression Model for CPU Power Prediction
- Finalized feature set and preprocessing for regression model
- Validated regression model performance on synthetic traces and exported model for use in autoencoder
Autoencoder Anomaly Detection Model
- Implemented sliding window AE on RF output
- Tuned initial architecture (window size, latent dimension, dropout, batch size)
- Ran verification sweep on fault windows
- Found strong overlap between normal and fault windows causing inconsistent fault detection
- Developing event based detection: detect at least once per fault event rather than per-window
Final Presentation
- Started drafting final presentation slides
Schedule & Progress
- On schedule: working on tuning ML model
Next Steps
- AE tuning + final presentation slides
Learning Strategies
- ML autoencoder: Learned to build autoencoder for anomaly detection
- Used Medium blogs, GitHub examples, and other people’s AE projects to understand typical structures and tuning strategies
- Referenced scikit-learn documentation and online tutorials for scaling, windowing, PCA plotting, and general debugging
- Already had background from ML classes but needed to apply to actual sensor data
- Testbed Assembly: Had previous experience with similar setup with Raspberry Pi 5 and sensors so integrating was familiar
- Had to learn how to run long data collection loops
- Communicate between Pi and Pico over UART
- Learn to flash code and debug UART on Pico with online tutorials and datasheets
- Practical Skills:
- Crimping connectors and assembling hardware setup
- Used YouTube videos, forums, and online guides
- Overall, most new knowledge came from informal learning
- Videos, online blog posts, GitHub repositories, datasheets, and example projects
