Speed monitoring and fault detection in bearings using an embedded piezoelectric transducer under speed-varying condition
Bibliographic record
Abstract
For more than three decades, vibration monitoring by accelerometers has been a common technique in the health monitoring of bearings and rotating machines. These sensors are typically mounted on the housing of the system to collect the vibration data. However, the susceptibility of accelerometers to surrounding noise and vibration has attracted more attention toward embedded sensors in bearings. Also, the current trend toward intelligent manufacturing and the Internet of Things (IoT) requires mechanical components with integrated sensors to monitor their health status. In this research, a previously developed piezoelectric transducer embedded in a bearing housing is further investigated for condition monitoring of bearings. By using this transducer, the rotational speed of the bearing in the variable speed condition is measured. The results show a great correlation between the estimated speed compared with an encoder. Moreover, the performance of the transducer in local fault detection in the speed-varying condition is investigated. According to the results, it can be concluded that this low-cost and self-sensing transducer can be successfully used for condition monitoring and speed measurement in bearings.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".