Bibliographic record
Abstract
Abstract This paper describes a pattern recognition (PR) technique, which uses learning vector quantization (LVQ). This method is adapted for practical application to solve problems in the area of condition monitoring and fault diagnosis where a number of fault signatures are involved. In these situations, the aim is health monitoring, including identification of deterioration of the healthy condition and identification of causes of the failure in real-time. For this reason a fault database is developed which contains the collected information about various states of operation of the system in the form of pattern vectors. The task of the real-time monitoring system is to correlate patterns of unknown faults with the known fault signatures in the fault database. This will determine cause of failure and degree of deterioration of the system under test. The problem of fault diagnosis may involve a large number of patterns and large sampling time, which affects the learning stage of neural networks. The study here also aims to find a fast learning model of neural networks for instances when a high number of patterns and numerous processing elements are involved. It begins searching for an appropriate solution. The study is extended to the enforcement learning models and considers LVQ as a network emerged from the competitive learning model through enforcement training. Finally, tests show an accuracy of 92.3 per cent in the fault diagnostic capability of the technique.
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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 | 0.000 |
| 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 | 0.001 | 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".