Looseness detection system of bolted joints using a VMD-based nonlinear transformation approach with deep residual network
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Bolted structures are subject to various vibrations, external forces and environmental factors, all of which can reduce their structural stability and compromise the integrity of bolted connections. Detecting bolt loosening in advance is crucial, as these effects often cause bolts to become loose, potentially leading to structural failure or collapse. However, identifying looseness in complex or large structures poses significant challenges, particularly when there is insufficient prior information about the loose-fit condition. To address this issue, the present study proposes a novel detection system for bolted joint looseness, employing a variational mode decomposition (VMD)-based nonlinear transformation (NT) approach integrated with a deep residual neural network, under several underlying assumptions. The proposed method utilizes VMD to decompose transverse vibrational modes into intrinsic mode functions (IMFs), selectively extracting signals with desired modes. The NT method is then applied to scale and shift the extracted signals, transforming them into a form that facilitates approximate classification. Image-based spectrograms are generated from the differences between transformed and reference signals, which are subsequently analyzed by the deep residual network. To validate the proposed method, several plates with bolted joints are considered.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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 it