A Multidimensional Data Fusion Neural Network for Damage Localization Using Ultrasonic Guided Wave
Bibliographic record
Abstract
Ultrasonic guided wave (UGW) based damage localization on plate-like composite structures plays a vital role in the structural health monitoring (SHM) of aircraft structures. Precisely locating the damage requires full utilization of high-dimensional UGW signals as well as low-dimensional transducer coordinates. However, current deep learning-based methods cannot incorporate transducer coordinates in the neural networks. To address this issue, this paper proposes a novel multidimensional data fusion neural network framework for damage localization on plate-like composite structures using UGW. The proposed framework includes an encoder and a Fourier feature projection head to integrate high-dimensional wave signals and low-dimensional coordinates. A multilayer perceptron is adopted as a decoder to learn features from the encoder and the projection head. Comprehensive experiments demonstrate that the proposed method achieves state-of-the-art results with less than 2 mm absolute distance error. Moreover, a discussion regarding data availability in the training process is performed. The proposed method demonstrates superior robustness over state-of-the-art methods with limited training data.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".