Acoustic emission sources localization and identification of complex metallic structures based on nearfield frequency space sparse decomposition
Bibliographic record
Abstract
Currently, structural health monitoring (SHM) of complex metallic structures based on the localization and identification of acoustic emission (AE) sources has become one of the most common condition monitoring method. However, existing methods are difficulty in accurately localizing and identifying AE sources generated by complex metallic structures that have been surface modified or machined. To overcome this problem, this paper presents a novel architecture named nearfield frequency space sparse decomposition (NFSSD) for localizing AE sources collected from complex metallic structures. Main contributions of the proposed NFSSD are to incorporate the decomposed subbands of AE signal in frequency into the traditional sparse decomposition (SD), which can extract more effective information and improve the identification of coherent AE sources. On this basis, NFSSD-based AE feature extraction scheme is further proposed for improving the accuracy and stability of AE source localization for complex metallic structures. First, all frequency point estimates of the original AE signal used to divide the subbands are obtained, where each frequency corresponds to the center frequency of the subband. Furthermore, the spatial spectrum of each subband signal is solved over the entire spatial domain, and the spatial spectrum of the signal is obtained to estimate the location of AE source. Two experimental results of coordinate-based AE source localization of complex metallic structures indicate that the proposed method has better AE source localization performance compared to conventional localization approaches. Specifically, the results show that the proposed approach can provide an effective theoretical reference for AE-based SHM of complex metallic structures.
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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 | 0.000 | 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".