Fault feature analysis and detection of progressive localized gear tooth pitting and spalling
Bibliographic record
Abstract
Abstract Fault feature analysis of gear tooth spalling plays a vital role in gear fault diagnosis. Understanding how fault features evolve as a fault progresses is key to fault severity assessment. Due to the complicated nature of gear meshing, fault features and their development as the fault severity progresses remain mostly unknown. The assessment of fault severity is generally based on the hypothesis that ‘the more severe the fault, the stronger the fault symptom’, an assumption that has not been experimentally validated. This paper provides a comprehensive, experimental analysis of the evolution of fault vibration features of a gear transmission with progressive localized gear tooth spalling. The effects of rotational speed on the vibration features of the gear transmission are analysed. Changes in fault features (e.g. periodic impulses and sideband phenomena) under different fault severity levels and speed conditions are compared. Results indicate that the number, amplitude and distribution of sidebands increase nonlinearly as the fault progresses. Based on feature analysis, a new health indicator of the mean of the n th order peaks is proposed to detect progressive localized tooth spalling. Results indicate that the proposed indicator shows very good performance for tracking the severity of progressive tooth spalling under different speed conditions.
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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 | 0.000 |
| 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".