Bladed disk crack detection through advanced analysis of blade time of arrival signal
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Health condition monitoring and fault diagnostics of turbo fan engines play significant roles in overall cost reduction and reliability enhancement of the aircraft system. Among various types of potential faults in a turbo fan engine, crack initiation and propagation in the bladed disks of engines caused by high-cycle fatigue under cyclic loads are typical ones that could result in the breakdown of the engines if not detected at an early stage. Reliable fault detection techniques are therefore required to detect impending engine malfunctions as well as unexpected failures that could otherwise lead to costly and/or catastrophic consequences. Although a number of approaches have been reported in literature, it still remains very challenging to develop a reliable technique to accurately estimate the health condition of bladed disks of engines. As such, this paper presents a new technique for engine bladed disk crack detection through advanced analysis of blade time-of-arrival signal. Two stages of signal processing are involved in this technique: 1) signal preprocessing for removing the noise caused by rotor imbalance; and 2) signal post-processing for identifying the location of the crack. The effectiveness of the developed technique is validated experimentally in a spin rig test.
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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 | 0.001 |
| 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) | 0.001 | 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 it