Performance Assessment of Gear Condition Indicators in Detecting Progressive Gear Tooth Crack
Bibliographic record
Abstract
Gear condition indicators are one of the most important gear transmission fault detection and diagnosis techniques. Various kinds of condition indicators for different kinds of gear fault types (e.g. tooth crack, wear, eccentricity, etc.) have been proposed in the past several decades. However, their relative effectiveness, especially in light of some newly proposed indicators, on gear fault detection or diagnosis has not been fully evaluated. Performance assessment of gear fault condition indicators is not only helpful in designing new advanced indicators but also important for the development of a reliable Condition Based Maintenance (CBM) system. The objective of this paper is to verify and compare the relative performances of twenty-one selected gear condition indicators as applied to a progressive gear tooth crack under constant load and speed working conditions. The main goals are to identify which indicators are sensitive to the fault or have the capability to detect the initial tooth crack and therefore to recommend the most effective gear condition indicators. Dynamic simulations were used to generate the vibration signals which reflect the real underlying vibration behavior of the transmission system. Based on the simulated results, the performances of the selected indicators under noise-free as well as various signal to noise ratio conditions were evaluated and compared. Results indicate that many of the selected indicators are effective for the detection of the progressive tooth crack only under noise-free conditions, and the indicators that only consider time or frequency domain features, such as RMS, Kurtosis, energy ratio, sideband index, are generally less able to detect a tooth crack at an early stage compared to the methods based on reconstructed signals, such as the NA4, FM4, M6A, M8A.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".