Bibliographic record
Abstract
A study is presented for detection and diagnostics of cracked rotors using soft computing techniques like adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANN) and genetic algorithms (GA). A simple model for a cracked rotor is used to simulate its transient response during startup for different levels of cracks. The transient response is processed through continuous wavelet transform (CWT) to extract time-frequency features for the normal and cracked conditions of the rotor. Several features including the wavelet energy distributions and the grey moment vectors (GMV) of the CWT scalograms are used as inputs for diagnosis of crack level. The parameters of the classifiers, ANFIS and ANN, along with the features from wavelet energy distribution and grey moment vectors are selected using GA maximizing the diagnostic success. The classifiers are trained with a subset of the data with known crack levels and tested using the other set of data (testing data), not used in training. The procedure is illustrated using the simulation data of a simple de Laval rotor with a ‘breathing’ crack for different crack levels during run-up through its critical speed. A comparison of diagnostic performance for the classifiers is presented. Results show the effectiveness of the proposed approach in detection and diagnosis of cracked rotors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".