Identifying Crack Parameters in Slow Rotating Machinery Using Vibration Measurements and Hybrid Neuro-Particle Swarm Technique
Bibliographic record
Abstract
Low-cycle fatigue-initiated cracks may result in failure in slow-rotating equipments. Online monitoring to identify such fault/crack parameters, namely crack size and crack location, would be critical in providing an early warning signal to the operator and would be used in calculating estimate about the remaining safe life of the equipment in operation. In an earlier study, a scaled-down slow-rotating washer drum was constructed to experimentally investigate the vibrations of a cracked rotor and/or drums. Cracks were simulated using the bolt removal method (BRM), and the vibration signals identifying signatures of certain cracks were measured. Thereafter, a 3D finite element model was used to solve the forward analysis of the inverse problem of crack identification. In this paper, the scaled-down experimental setup is introduced to cracks at different locations of the drum/rotor. Vibration signals identifying signatures of such cracks are measured. Since noisy signals, similar patterns of faults, and similar vibration fault signals create particular challenges for feature extraction systems, two techniques for feature extraction are considered and compared in this work. The fast Fourier transform (FFT) of the vibration signals showing variation in amplitude of the harmonics as time progresses are presented for comparison with the full time signal feature extraction. A hybrid particle-swarm artificial Neural Networks (neuro-particle swarm) is used to identify both the crack size and crack location. The hybrid neuro-particle swarm technique is compared with the previously investigated fuzzy genetic algorithms.
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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 | 0.001 | 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 | 0.001 |
| 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".