Improving the Accuracy of Detecting Signs of Combustion Instability by Using Anomaly Detection
Bibliographic record
Abstract
In this paper, a simple combustion device consisting of a premixed burner, a rectangular cylinder, and a visualization window was used to measure the pressure fluctuation level and flame images while varying the flame position and operating conditions.A Convolutional Autoencoder (CAE) was applied to the acquired images to extract the features of the images.The images reconstructed from the extracted features and the original acquired images were then used to define the Combustion Instability Index (ΔCAE TI_err ), which can be used to quantify the flame conditions.By organizing the correlation between the proposed Combustion Instability Index and the combustion oscillation levels, we evaluated the possibility of detecting signs of increase in the combustion oscillation.The results showed that the proposed Combustion Instability Index and the combustion oscillation level were highly correlated.Using Grad-CAM data analysis, which enables visualization of the Combustion Instability Index on a two-dimensional plane, the mechanism that causes the increase in the combustion oscillation level was discussed by evaluating the effects of operating conditions on the flame distribution.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".