Fault Diagnosis of Electric City Bus High-Voltage Load System Based on Multidomain Sparse Representation
Bibliographic record
Abstract
This article presents a system-level fault diagnosis scheme for the high-voltage load system of electric city bus (ECB). First, a predesigned excitation signal generated by the battery system is injected into the high-voltage load system during parking, and the response signal is captured by high-speed data acquisition device. Then, time domain features, frequency domain features, and time-frequency domain features are extracted from the response signal, in addition, novel geometry features are also extracted from the response signal, which are composed of shape features (SF), dynamic performance (DP) indices, and frequency spectrum (FS) features. Further, in time domain, frequency domain, time-frequency domain, and geometry feature domain, the above features extracted from labeled samples are utilized to construct dictionaries, and sparse representation are conducted for testing samples to obtain sparse vectors. Finally, based on AdaBoost, the sparse vectors obtained from the four domains are fused, and the fault diagnosis is realized by analyzing the nonzero elements distribution of the fused sparse vector. Validations for the proposed method are conducted based on datasets obtained from AMESim simulation, ECB test rig, and real ECB, the diagnosis accuracy are 98.33%, 94.00%, and 92.50%, respectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".