Empirical mode decomposition and its variants: a review with applications in structural health monitoring
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 Structural health monitoring (SHM) is one of the most emerging approaches for early damage detection, which leads to improved safety and efficient maintenance of large-scale civil structures. Data-driven vibration-based SHM techniques rely on sophisticated signal processing methods to analyze and interpret the complex measured data collected from the instrumented structures. Empirical mode decomposition (EMD) is one of the robust time-frequency decomposition techniques that has been widely used in SHM. Numerous studies have used EMD and its variants in different applications specific to structural modal identification and damage detection, which have been presented in various academic journals, conference papers, and technical reports. This paper presents a comprehensive and systematic review and summary of applications of EMD and its variants that have been extensively implemented in SHM. A brief background and illustration of EMD and its variants are presented first to show their performance under various cases, followed by a detailed literature review of their recent applications specific to SHM.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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