Automated Fault Diagnosis for Small Gas Turbine Engines
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
In one possible model of distributed power generation a large number of users will operate individual, gas turbine powered, cogeneration systems. These systems will be small, relatively inexpensive, and installed in locations without ready access to gas turbine maintenance experts. Consequently an automated method to monitor the engine and diagnose its health is required. To remain compatible with the low cost of the power system the diagnostics must also be relatively inexpensive to install and operate. Accordingly a minimum number of extra sensors should be used and the analysis performed by a common personal computer system. The current work automates the diagnosis of component faults by comparing the engine’s operating trends to the trends for known faults. This allows the relative percentage chance of each fault occurring to be determined. The likelihood of each fault is then compared, to determine which component is degrading. The technique can be adapted to compare the engines historic operating trend or a single operating point. In this initial work a computer model was used as a test bed and 5 faults were introduced individually. The technique successfully diagnosed the faulty component using either the operating trend or a single operating point.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.000 | 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