SYNTHETIC · Generated records
Sensor Stream Simulator.
Repeatable inputs for exercising a future measurement workflow.
- Engineering question
- How can known, repeatable inputs exercise an analysis path before hardware supplies data?
- My contribution
- Developed eight seeded synthetic scenarios and file generation with AI assistance.
- Current evidence
- SYNTHETIC CSV/JSON records; 35 software tests passed in the Codex check on October 10, 2026.
- Next step
- Use the generated scenarios to inspect analysis behavior while real instrument qualification stays separate.
Problem and my contribution
I developed this generator with AI assistance to test data-processing behavior before hardware supplies measurements. Known scenarios and seeded randomness make it possible to repeat an input and inspect how downstream software handles it.
Implemented methods
Eight modes include normal operation, gradual temperature increase, restricted flow, pressure-drop increase, drift, missing values, pump failure, and a leak event. Simplified relationships connect pump command, relative resistance, flow, pressure drop, and temperature rise. Outputs include CSV or JSON with scenario labels.
Verification
On October 10, 2026, Codex ran the current repository tests: 35 passed. The checks cover scenarios, repeatability, expected trends, timestamps, file output, and command-line behavior.
Interpretation and limits
The imposed synthetic heat load and anomaly labels come from the generator. They are useful fixtures for exercising software, but do not represent measured electrical input, a pump curve, a hydraulic solver, or validated fault detection.
Current status and next step
The synthetic generator is implemented. It supports analysis development while real acquisition and instrument qualification remain separate engineering tasks.