Failure Analysis of Aircraft Landing Gear Components
Bibliographic record
Abstract
Abstract Despite extensive aircraft landing gear design analyses and tests performed by designers and manufacturers, and the large number of trouble-free landings, aircraft users have experienced problems with and failures of landing gear components. Different data banks and over 200 failure analysis reports were surveyed to provide an overview of structural landing gear component failures as experienced by the Canadian Forces over the last 20 years on more than 20 aircraft types, and to assess trends in failure mechanisms and causes. Case histories were selected to illustrate typical problems, troublesome failure mechanisms, the role of high strength aluminum alloys and steels, and situations where fracture mechanics analyses provided insight into the failures. The two main failure mechanisms were: fatigue occurring mainly in steel components, and corrosion related problems with aluminum alloys. Very few overload failures were noted. A number of causes were identified: design deficiencies and manufacturing defects leading mainly to fatigue failures, and poor materials selection and improper maintenance as the principal causes of corrosion-related failures. The survey showed that a proper understanding of the failure mechanisms and causes, by thorough failure analysis, provides valuable feedback information to designers, operators and maintenance personnel for appropriate corrective actions to be taken.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".