Structural Health Monitoring of Truss Type Structures Using Statistical Approach
Bibliographic record
Abstract
This paper proposes an effective statistical based vibration health monitoring technique using Auto Regressive (AR) parameters and Support Vector Machine (SVM) for truss type structures. The finite element method has been utilized to obtain acceleration response signals of a space truss structure under random excitations. The signals are then processed to extract their AR parameters as the feature vectors in which the AR parameters of the healthy structure are considered to be the reference baseline data. A Damage Index is then defined to be the standard deviation of the feature vectors from the baseline data. The proposed index provides an effective tool to detect the damage in the structure. It is shown that using only one sensor, it is still possible to accurately detect the damage. To locate the damage, data classification technique based on Support Vector Machine (SVM) has been employed. It is shown that SVM can successfully classify different signals extracted from the structure. Finally extensive sensitivity analysis has been performed to study the effect of different parameter such as crack size, number of sensors and AR parameter numbers on the accuracy of detection and localization processes.
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How this classification was reachedexpand
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) | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".