A Feature-Engineering Approach to Support Vector Machine-Based Damage Detection in Lead Zirconate Titanate Ceramics Via Point-Contact Wavefield Measurement
Bibliographic record
Abstract
Abstract In this study, a machine learning-based detection and localization of localized damage in lead zirconate titanate (PZT) ceramics is developed. A point-contact excitation and detection method is employed to excite and detect acoustic wave signals from the PZT sensor. The signals are analyzed using non-destructive evaluation techniques. The significant features of wavelet transform coefficients, auto-regressive modeling parameters, peak amplitude, peak location, and wave energy are extracted from the waveforms. These features capture the salient properties of the acoustic response that change in the presence of structural damage. A trained support vector machine classifier is used to distinguish between damaged and healthy regions based on the extracted features. Classification achieved a recall of 92.7% and a precision of 86.0% for the minority damaged class. However, the method is compromised at the center of the samples, where the wave energy is the highest and the signal originates. Furthermore, the thresholding method used in data labeling can be sensitive to local anomalies, potentially leading to misclassification. Despite these challenges, the proposed framework supports a scalable and robust real-time damage detection system. By integrating machine learning, point-contact acoustic sensing, and signal processing, this study contributes to the development of automated and accurate structural health monitoring techniques for smart sensing systems.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".