Diagnostic fault detection for internal combustion engines via norm based map projections
Bibliographic record
Abstract
One proven technique for monitoring health of a sealed internal combustion engine is to analyze combustion pressure cycle curves of the individual cylinders. Most techniques that are available are either overly simplistic or rely on artificial intelligence based methodologies such as neural networks. While neural network based methods can be useful, there is normally no quantitatively hard way to determine how accurately a trained neural network represents the desired goal. For this reason, neural networks have little real acceptance by industrial communities that deal with critical applications. This paper describes a technique developed for detecting combustion pressure cycle related faults in diesel engines. This method has been developed at the Pennsylvania State Universities, Applied Research Laboratories, Complex Systems Monitoring and Automation Department and applied to a fully instrumented diesel engine test bed. The new methodology utilizes pressure curve information derived from reliable and relatively inexpensive optical fiber based pressure sensors. The technique outlined in this paper uses a combination of norm based and statistical methods to develop a fault analysis map for particular internal combustion engines. A fully instrumented diesel engine test bed allows for generation of training data sets consistent with actual engine operation. Results from this technique applied to test bed data not used during development of the map show results closely match seeded fault conditions.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".