Crack Detection in Shafts Using Mechanical Impedance Measurements
Bibliographic record
Abstract
In this study, experimental and analytical investigations are carried out on an overhanging cylindrical shaft carrying a propeller at the cantilever end, in order to identify the crack existence in shafts using the mechanical impedance approach. Also the experimental study uses the modal analysis software, LMS Test LabTM, for measuring and analyzing the response results from un-cracked and cracked shafts. In the numerical study, both the un-cracked and the cracked shafts (with varying crack depths) are modeled by finite element procedure. 3-D iso-parametric elements (element types 186 and 187), available in the ANSYS FEM program, are utilized to model the system. The impedance and velocity frequency response functions are used to identify the crack depth in the shaft system. Impedance and mobility were measured and simulated in the vertical direction for the resonant frequencies and anti-resonant frequencies. The experimental results are used to validate the numerical results. A better crack detection procedure was obtained by the plot of the slope of the non-dimensional frequency ratio (in the resonant and anti-resonant regimes) vs. the non-dimensional crack depth ratio. By this method crack presence could be definite detected when the non-dimensional crack ratio is greater than 0.20 to 0.25. In addition monitoring of the lower torsional frequency indicated the crack presence even from the beginning stages.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".