Failure Mode and Effects Analysis of Compressor Blades of Aeroengines Using Dempster-Shafer Evidence Theory
Bibliographic record
Abstract
Compressor blades are a major component of an aeroengine. Failure mode and effects analysis (FMEA), especially, the risk priority order of failure modes, is essential in its design. The risk priority number (RPN) has been extensively used to the risk priority order of failure modes. When multiple experts give their different risk evaluation information to one failure mode, which may be imprecise and uncertain, the traditional RPN cannot deal with the problem. In this paper, the modified Dempster-Shafter (D-S) is adopted to aggregate the different evaluation information by considering multiple experts’ evaluation opinions, multiple failure modes and three risk factors respectively. The simplified discernment frame is proposed according to the practical application. Moreover, the mean value of the new RPN is used to risk priority order of multiple failure modes. Finally, the method is used to deal with the risk priority evaluation of the failure modes of compressor blades of an aeroengine under multiple sources of different and uncertain evaluation information. The consequence of the method is rational and efficient.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".