Bearing fault detection and fault size estimation using an integrated PVDF transducer
Bibliographic record
Abstract
Vibration analysis of bearings by accelerometer sensors is one of the most common techniques in bearing condition monitoring. However, the susceptibility of accelerometers to noise and vibration of other machines creates practical difficulties in detecting bearings faults in applications with noisy settings. To overcome this issue, the development of integrated sensors in bearings with a short transmission path has been an emerging research area to enhance fault detection in bearings. According to the literature, polymer-based piezoelectric transducers can be a proper transducer for this application, although their performance has not been thoroughly investigated. Therefore, in this research, using an integrated PVDF transducer in a cylindrical roller bearing is proposed to detect the local fault and estimate the size of the damage. Through experimental analysis in a bearing test system, the performance of the PVDF is evaluated. According to the results, the fault symptoms can be accurately captured in the voltage signal of the PVDF transducer under constant and variable rotational speeds. Also, by analyzing the behavior of a roller over a local fault and comparing it with the measured voltage signal, the fault size estimation with an accuracy of ±0.025 mm is achieved.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".