Simulation of a Cylindrical Roller Bearing with an Embedded Piezoelectric Sensor for Local Fault Detection
Bibliographic record
Abstract
Local fault detection in bearings through accelerometer has been one of the most fundamental condition monitoring techniques for three decades. Nevertheless, the sensitivity of accelerometers to surrounding noises from other components/machines and a long transmission path between the sensor and bearing has attracted more attention toward using embedded sensors and measurement of local strain rather than acceleration. These days, utilizing embedded smart materials, such as low-cost piezoelectric sensors, can benefit different industries to detect abnormal conditions in machines or structures. In some cases, these abnormalities show up in the form of sudden strain changes that is detectable by piezoelectric materials. Therefore, in this research, using embedded piezoelectric sensors in a bearing housing with a short transmission path is proposed to detect abnormalities due to a local fault. Through numerical simulation in ANSYS APDL, the dynamic of a cylindrical roller bearing in the healthy and defective conditions is investigated. According to the results, the existence of a local fault affects the radial strain changes and proportionally changes the generated voltage signal. These changes (fault symptoms) are investigated in the time and frequency domains for fault detection.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".