Development of a Web-Based Diagnostic Tool Using Acoustic Testing and Computer Vision
Bibliographic record
Abstract
Acoustic testing is a technology that covers various machinery failure modes, including bearing and gear failures. This technology is superior to vibration analysis for gear and bearing condition monitoring. This paper aims to offer the maintenance world a critical technological advance by developing a web-based tool that, using pretrained convolutional neural networks and spectrograms, allows the diagnosis of gearboxes from recordings obtained with industrial acoustic testing tools. The resulting model is tested against human specialists to assess its actual world performance. A modified agile methodology was implemented to develop the research systematically. The type of approach is mixed since it has qualitative parts, such as specialists involved in obtaining the ultrasonic data and classifying them, and quantitative parts, such as validating the precision of the model based on established validation metrics. By using a pretrained model and then performing a fine-tuning with heterodyne ultrasound recordings from gearboxes in good and bad condition, a training accuracy of 93% was achieved. Then, tests were carried out to validate false positives and negatives in which it was possible to obtain 0% and 6.7% scores, respectively. This model was incorporated on a web platform to create the diagnostic tool whose input variable is the recording, and the output variables are its spectrogram, the prediction of whether it is in good or bad condition, and the probability of both possibilities.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".