Cycle-by-Cycle Exhaust Temperature Monitoring for Detection of Misfiring and Combustion Instability in Reciprocating Engines
Bibliographic record
Abstract
This paper describes a means of achieving cycle-by-cycle combustion monitoring of reciprocating engines without the use of cylinder pressure sensors. This approach is intended primarily for engines that are not equipped with indicator passages (that would facilitate the installation of cylinder pressure sensors) but are (or can be) equipped with fittings for individual cylinder exhaust thermocouples. The monitoring system uses rugged exhaust temperature probes and advanced signal processing and analysis to detect cycle-by-cycle variations in exhaust temperatures and correlates these with conventional combustion analysis parameters. The system is particularly useful for detecting the deteriorations in combustion stability that precede misfiring as well as individual misfire events if they occur. Engine test results are presented showing the correlation between the exhaust temperature signal and parameters based upon cylinder pressure measurements. The ability to detect low level combustion instability and isolated, individual misfires has been demonstrated on a 95 liter V12 industrial natural gas engine. It as also been shown that successful acquisition of high fidelity exhaust temperature signals for the combustion analysis can be achieved in the presence of the high levels of electromagnetic interference typical of a power generation facility.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".