An Adaptive Fault Diagnosis of Electric Vehicles: An Artificial Intelligence Blended Signal Processing Methodology
Bibliographic record
Abstract
This article demonstrates an innovative design of a sensorless technique to diagnose, monitor, and broadcast faults in an electric vehicle’s (EV) propulsion operating conditions. By utilizing the artificial intelligence with a signal processing mixed clustering technique, an onboard health monitoring system (HMS) has been presented. The clustering technique uses a data-mining approach to prevent future failures for predictive maintenance planning, which is novel. For example, the propulsion inverter is equipped with a diagnostic system that uses the proposed algorithm to compare the reference gate-driving signal with the actual output voltage of the voltage source inverter (VSI). This article presents different failure scenarios of the inverter and demonstrates the capability to be applied to other components, such as brakes and motors. To validate the proposed technique, the necessary algorithm calculations, simulation, and laboratory prototype results are provided. The proposed work is proven accurate with fast response in healthy and faulty conditions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".