Structural Health Monitoring of Aeroengines Using Transmissibility and Bond Graph Methodology
Bibliographic record
Abstract
In recent years, due to increase in demand for structural safety in various engineering disciplines such as aerospace, researchers have focused on developing vibration-based methodologies for monitoring and detecting structural damage in various structures. Safety and performance of an aging structure is a prominent issue, occurring in aircraft, engines, and public infrastructures. There have been significant recent developments in the field of structural health monitoring (SHM) [1–9]. SHM provides a means to monitor structural safety in a continuous manner. The main objective of SHM can be defined as the assessment and monitoring of the structural safety and operational condition to detect any anticipated damage or faults that are beginning to occur or ones that have already propagated and advanced in the structure to prolong the operational lifetime of the structure in a safe manner. This goal is achieved while reducing detection and maintenance costs to significantly enhance and improve on the safety of the structure by a providing an early detection tool to detect structural damage. Moreover, aside from the improved safety and performance, the obtained knowledge from SHM can be utilized to improve on the early designs of the structure. Structural aging is caused by continuous loading conditions (low and high loading forces), leading to fatigue cracks in the structure [10]. The exposure to continuous loading conditions leads to crack growth in the structure, leading to catastrophic failures. This is shown by Figure 10.1.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".