A −40 to 250°C Triple Modular Redundancy Temperature Sensor for Turbofan Engines
Bibliographic record
Abstract
Turbofan engines using distributed control architecture require sensors and electronic instrumentation located in the engine compartment to simplify system design, improve reliability, and ease signal multiplexing. Designing a wide range temperature sensor which works reliably regardless of process variation can be extremely challenging. Using a bandgap-like topology with PiN diodes as the sensing element, this work proposes a -40 to 250 °C temperature sensor in the IMS 0.35 μm SOI technology. To circumvent the process variation challenge, a triple modular redundancy is proposed for the sensing element combined with a weighted voter and digital circuitry calibration. The proposed sensing element supplies the analog-to-digital conversion (ADC) with a VREFhaving a typical-mean temperature coefficient of 62 ppm/°C, 24 ppm/°C for the best corner, and 213 ppm/°C for the worst corner. An average VCTAT sensitivity is estimated at 0.282 mV/°C among all corners. After a 2-point temperature calibration, the ADC quantization step presented a negligible variation over temperature, approximately 1.0 mV among all corners, assuring a 0.28 °C of temperature inaccuracy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".