Fault Diagnosis of Sensors for Multi-stack Fuel Cell Thermal Management Subsystem Based on UKF
Bibliographic record
Abstract
In Multi-stack fuel cell system (MFCS), the thermal management subsystem has various heat dissipation structures and heat dissipation forms, and the stability and accuracy of its operation are important indicators to ensure the safety of the system. In this paper, a water-cooled integrated MFCS thermal management subsystem model is established, and a sensor fault diagnosis method based on Unscented Kalman Filter (UKF) is proposed for the sensor fault in the thermal management subsystem, which adopts the Unscented Transform for the nonlinear system and obtains the estimated value through three processes of prediction, update and iterative calculation. The difference calculation method is adopted to calculate the fused residuals of the UKF estimates and the measured values of the thermal management subsystem sensors to obtain fault information for single or multiple sensors. The results show that the fault diagnosis using the difference method of UKF estimate and the sensor measurements residual signal for the variation of MFCS thermal management subsystem structure and signal acquisition can quickly determine the type and location of single or multiple sensor faults in the thermal management subsystem.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".