A Deep Learning Approach for Fault Diagnosis of Hydrogen Fueled Micro Gas Turbines
Bibliographic record
Abstract
Hydrogen fueled gas turbines are susceptible to rigorous health degradation in form of corrosion and erosion in the turbine section of a retrofitted gas turbine due to drastically different thermophysical properties of flue gas stemming from hydrogen combustion. In this context fault diagnosis of hydrogen fueled gas turbines becomes indispensable. To authors knowledge, there is a scarcity of fault diagnosis studies for retrofitted gas turbines considering hydrogen as a potential fuel. The present study, however, develops an artificial neural network (ANN) based fault diagnosis model using MATLAB environment. Prior to fault detection, isolation and identification modules, physics-based performance data of 100 kW micro gas turbine (MGT) was synthesized using GasTurb tool. ANN based classification algorithm showed a 99.4% classification accuracy of fault detection and isolation. Moreover, the feedforward neural network-based regression algorithm showed quite good training, testing and validation accuracies in terms of root mean square error (RMSE). The study revealed that presence of hydrogen induced corrosion fault (both as single corrosion fault or as simultaneous fouling and corrosion) led to false alarms thereby prompting other wrong faults during fault detection and isolation modules. Additionally, performance of fault identification module for hydrogen fuel scenario was found to be marginally lower than that of natural gas case due to assuming small magnitudes of faults arising from hydrogen induced corrosion.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".