Vehicle gearbox fault diagnosis using noise measurements
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Noise measurement is one of many technologies for health monitoring and diagnosis of rotating machines such as gearboxes. Although significant research has been undertaken in understanding the potential of noise measurement in monitoring gearboxes this has been solely applied on any types of gears (spur, helical, ..etc.). The condition monitoring of a lab-scale, single stage, gearbox, represents the vehicle real gearbox, using non-destructive inspection methodology and the processing of the acquired waveform with advanced signal processing techniques is the aim of the present work. Acoustic emission was utilized for this purpose. The experimental setup and the instrumentation are present in detail. Emphasis is given on the signal processing of the acquired noise measurement signal in order to extract conventional as well as novel parameters potential diagnostic value from the monitoring waveform. The evolution of selected parameters/features versus test time is provided, evaluated and the parameters with most interesting diagnostic behavior are highlighted. The present work also reports the results concluded by long term (~ 6.0 h) experiments to a defected gear system, with a transverse cuts ranged from 0.75 mm to 3.0 mm to simulate the tooth crack. Different parameters, related by the analysis of the recording signals coming from acoustic emission are presented and their diagnostic value is discussed for the development of a condition monitoring system. Copyright © 2011 International Energy and Environment Foundation - All rights reserved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it