A Hybrid Model-Data Vehicle Sensor and Actuator Fault Detection and Diagnosis System
Bibliographic record
Abstract
This paper proposes a hybrid model/data fault detection and diagnosis system applicable to any vehicle sensor and actuator. This system works based on comparing the measurements of a target sensor or the desired control actions of a target actuator with their estimations. These estimations are obtained by a hybrid estimator developed based on the integration of model-based and data-driven estimators leveraging the strength of each estimator. Considering the weakness of pure data-driven estimators in confronting unknown conditions, a self-updating dataset is proposed to learn new cases. After fault detection, the estimations of the hybrid estimator are used to reconstruct sensor data or find the level of actuator failure. To evaluate the performance of the proposed hybrid fault detection and diagnosis system, it is applied to a vehicle’s lateral acceleration sensor and traction motor. The results of experimental tests conducted on an all-wheel-drive vehicle show the effectiveness of the algorithm in detecting and quantifying faults in the target component.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".