Gearbox failure diagnosis based on vector autoregressive modelling of vibration data and dynamic principal component analysis
Bibliographic record
Abstract
An effective gearbox failure diagnosis helps prevent catastrophic gearbox failure and can contribute to significant economic benefits. This paper proposes a gear failure diagnosis method based on vector autoregressive modelling of high-frequency vibration data, dimensionality reduction applying dynamic principal component analysis (PCA) and condition monitoring using a multivariate control chart. After extracting useful information from the vibration data obtained from distinct directions via dynamic PCA, a failure diagnosis scheme is implemented and tested using real gearbox vibration data. It is shown that the failure diagnosis scheme can indicate the gear teeth failure pattern when the gear is damaged, which has not been demonstrated in the previous studies. For a comparison, PCA is applied to the same data set. The results show that the advantages of dynamic PCA over PCA for failure diagnosis using vibration data consist not only in indicating more accurately the occurrence of incipient fault and the actual gear condition, but also in a much lower false alarm rate.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".