Early fatigue failure detection in composites using autoencoder-based anomaly detection
Bibliographic record
Abstract
Despite the widespread adoption of composite materials across various industries, accurately evaluating their durability—particularly under fatigue loading—remains a major challenge. A key difficulty lies in the substantial scatter in fatigue life among seemingly identical specimens. This variability elevates the risk of sudden, catastrophic failures and necessitates conservative, schedule-based maintenance plans that are designed around worst-case scenarios. This study presents a novel approach to identify composite specimens with short fatigue lives at the early stage of loading by integrating piezo-resistivity-based structural health monitoring (SHM) with autoencoder-based anomaly detection techniques. Glass fiber–epoxy composites, made electrically conductive by incorporating carbon nanotubes (CNTs), were subjected to fatigue loading until failure, while their electrical resistance (ER) was continuously monitored. The ER data from the early stage of loading were extracted and used to train and optimize autoencoders to detect early fatigue failure (i.e., short-life specimens). The results demonstrated an F1 score of 95 % and an accuracy of 97 % in correctly identifying short-life specimens, underscoring the effectiveness of the proposed approach.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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 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".