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Jacob’s Status Report for 12/06/2025

My work this week was concentrated on the team video and the final report. I focused on updating some of the report’s mandatory sections: the Abstract,  Architecture, and ethical considerations. This made sure they appropriately depicted the system as it was constructed. Incorporating public health, safety, welfare, and the global, cultural, social, environmental, and economic aspects from our previous work.

I also assisted with organizing how we present the system and planning and filming parts of the team video, such as shots of the cooling loop.

The project is still on track for the final demo. Next week, we’ll finalize the video and the remaining edits to the report.

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Aidan’s Status Report for 12/06/2025

This week, I began progress on the team video, which included planning our formatting, videography, and timeline to successfully showcase our project with sufficient detail. I also assisted with recording shots of the cooling loop for use in the video. Additionally, I  researched ethical considerations to eventually integrate into the final report. This involved researching and drafting this section of the final report to ensure all requirements are met within the context of our project. Working on this earlier ensured that our team had extra slack prior to the final demo if desired.

The team is currently on track for the final demo, and we are fine tuning the model through data recollection to ensure a working product. The schedule is on track, and we are looking forward to the final demo!

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Team Status Report for 12/06/2025

Accomplishments

  • Testbed coolant flush: Flushed out cooling loop because of yellow fluid and contaminants
  • Data collection: Recollected normal dataset due to ambient temperature drift and coolant loop differences affecting inference
  • ML model development: Retrained regression and autoencoder using updated normal data
    • Updated autoencoder architecture to use delta temperature features only to lessen effect of ambient temperature changes
  • Final deliverables: Started planning Final Report, Video, and Demo
    • Took video of testbed for Final Video
    • Split up Final Report sections

Significant Risks

  • Model sensitivity: Since model is designed to detect small deviations, small drifts in ambient temperature, water levels, and other factors can cause the model to alert an anomaly
    • Switched to delta temperature only features (no absolute temperatures)
    • Tuned thresholds based on power levels

Design Changes

  • No major design changes this week

Schedule Changes

  • Project remains on track
    • ML model tuning in progress

Testing and Results

  • Servo Valve Flow Restriction Test: Ran servo through different PWM duty values and measured flow rate to map duty value to flow rate reduction
    • Result: Mapped duty value to flow rate reduction with fit equation
  • Heater / Power Delivery Test: Applied PWM values to the SSR and confirmed heater power using RMS voltage measured on oscilloscope
    • Design Change: Found that power supply rating too low and had to purchase higher wattage power supply (300W)
    • Result: Commanded power is accurate to actual measured Vrms
  • Regression Model Prediction Accuracy Test: Compared predicted vs. measured CPU power on normal datasets
    • Design Change: Replaced original linear regression model with a Random Forest after benchmarking several models
    • Result: Random Forest achieved RMSE = 2.53 W
  • Autoencoder Model Latent Space Test: Examined latent space separation between normal and fault windows and measured FPR and FNR
    • Result: Model successfully differentiates normal from fault data
      • FPR: 0.38%
      • FNR: 0.64%
  • Classification Model Accuracy Test: Evaluated classifier performance on distinguishing flow faults vs. power faults
    • Result: Model successfully classifies flow fault and power fault
      • FPR: 0.00%
      • FNR: 1.06%
  • Latency Test: Measured timing from inference start to alert generation
    • Result: Model meets 1s latency requirement
      • Regression = 141 ms
      • Autoencoder = 363 ms
      • Classifier = 9 ms
      • End-to-end = 825 ms
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Kristina’s Status Report for 12/06/2025

Recollected Normal Condition Data

  • Ambient temperature shifted noticeably from previous data collection runs
  • Repeated full normal data collection loop to collect data at current ambient temperature

Full Flush of Cooling System

  • Coolant turned yellow and contained some kind of brown particles, likely residue from radiator
  • Fully drained the loop, flushed several times, and replaced water

Retrained Model with New Dataset

  • Updated preprocessing steps, retrained regression and autoencoder models using new data
  • Updated model architecture to use only delta temperature features
    • Reduce model sensitivity to ambient drift
  • Began integrating updated models into live inference loop

Schedule & Progress

  • On schedule for final demo

Next Steps

  • Finish testing live inference loop
  • Complete final report and final video