Structural Health Monitoring of Aeroengines Using Transmissibility and Bond Graph Methodology
Bibliographic record
Abstract
Due to increasing demand for structural safety and reliability in various industrial sectors, a great degree of effort has been made by researchers to propose various dynamic- and vibration-based theories to monitor and detect structural defects and damage that occur over the operational lifetime of a system. This chapter proposes a comprehensive and novel study of structural health monitoring analysis in conjunction with the bond graph theory for implementation on large structures, specifically aeroengines. Bond graph methodology has been shown to be a useful technique in performing structural health monitoring by reducing testing and damage detection costs by reducing associated labor effort. In this chapter, a reduced aeroengine model has been developed. Using the developed bond graph model of the aeroengine, the governing dynamic equations of motion were determined. By implementing the global transmissibility concept, structural health monitoring and damage detection were implemented on the proposed aeroengine model. Using the obtained frequency response functions, defects and damage in the structure were theoretically detected and classified. Moreover, using the obtained transmissibility functions, damage indicator factors were determined to be of importance in localizing the damage within the structure. It was also shown that the damage indicator values can be used to determine the extent of damage and defect in the aeroengine when compared with experimental data. In addition to safety improvement, the obtained knowledge from the proposed analysis can be utilized to implement early design modification and guidelines based on the predetermined safety factors of the aeroengine, hence significantly improving reliability and operational lifetime of the aeroengine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".