Development of in-process engine defect detection methods using NVH indicators.
Bibliographic record
Abstract
This study was undertaken to investigate and develop engine defect detection methods using NVH indicators for future implementation into an on-line test system in a production environment. These methods utilized noise and vibration measurements collected from a variety of transducers to successfully detect lower-end engine defects. The on-line experimental testing of 5.4L V8 engines was conducted at one of the in-process Cold Test stations at the Ford Windsor Engine Plant. Transducers used included accelerometers, microphones, knock sensors and a laser vibrometer. The optimal measurement locations were found to be at each of the 4 locating lugs of the engine. Baseline measurements were made and based upon these results, control limits were established regarding the acceptable noise and vibration levels an engine can exhibit. A fault diagnosis algorithm that utilized variance analysis and RMS values was developed to detect lower-end engine defects. The algorithm was successful in identifying defect-free engines as well as detecting lower-end faults such as a non-machined cylinder bore, a cylinder bore containing a deep groove and connecting rod knock. The transducers found to be most effective in detecting noise and vibration were the accelerometer and the laser vibrometer. The knock sensor and microphone were found to be inconsistent in their ability to detect noise and vibration. Therefore, it was concluded that the development of an on-line test system that can successfully diagnose engine defects through the use of NVH indicators is feasible and would ultimately reduce the number of defective engines being produced.Dept. of Mechanical, Automotive, and Materials Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2002 .L45. Source: Masters Abstracts International, Volume: 41-04, page: 1181. Adviser: R. Gaspar. Thesis (M.A.Sc.)--University of Windsor (Canada), 2002.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".