A framework with nonlinear system model and nonparametric noise for gas turbine degradation state estimation
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
Modern health management approaches for gas turbine engines (GTEs) aim to precisely estimate the health state of the GTE components to optimize maintenance decisions with respect to both economy and safety. In this research, we propose an advanced framework to identify the most likely degradation state of the turbine section in a GTE for prognostics and health management (PHM) applications. A novel nonlinear thermodynamic model is used to predict the performance parameters of the GTE given the measurements. The ratio between real efficiency of the GTE and simulated efficiency in the newly installed condition is defined as the health indicator and provided at each sequence. The symptom of nonrecoverable degradations in the turbine section, i.e. loss of turbine efficiency, is assumed to be the internal degradation state. A regularized auxiliary particle filter (RAPF) is developed to sequentially estimate the internal degradation state in nonuniform time sequences upon receiving sets of new measurements. The effectiveness of the technique is examined using the operating data over an entire time-between-overhaul cycle of a simple-cycle industrial GTE. The results clearly show the trend of degradation in the turbine section and the occasional fluctuations, which are well supported by the service history of the GTE. The research also suggests the efficacy of the proposed technique to monitor the health state of the turbine section of a GTE by implementing model-based PHM without the need for additional instrumentation.
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 | 0.001 | 0.001 |
| 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) | 0.000 | 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