Development of compact smart bearing and novel hybrid feature assessment for weak defect identification
Bibliographic record
Abstract
In this study, a compact smart bearing with self-sensing and condition monitoring function is proposed. A traditional bearing embedded in a piezoelectric transducer ring with a segmented electrode design is prototyped and tested under different working conditions. Its compact design ensures compatibility for integration with different systems. The segmented electrodes enable possible multidirectional data acquisition and piezoelectric energy generation, suggesting potential integration with Internet of Things (IoT) platforms. Utilizing the high sensitivity, fast response and high signal resolution of the piezoelectric material in conjunction with a novel design that enables close contact between the piezoelectric transducer and the bearing, the smart bearing demonstrates effective performance in detecting weak bearing defects signals. A novel feature characterising method is proposed, and a hybridised feature selection method is employed for reducing the dimension of feature subsets and ensuring defect identification accuracy. A classification model for the identification of defects is developed based on a Long Short-Term Memory (LSTM) network. The performance of the smart bearing and the method for identifying the defects are evaluated through experiments to demonstrate the potential for practical applications. A preliminary experiment for energy harvesting using the smart bearing has been conducted, and it proves the potential to sustain power.
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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) | 0.001 | 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".