Fault Diagnosis of Single-Variate Systems Using a Wavelet-Based Pattern Recognition Technique
Bibliographic record
Abstract
A pattern recognition-based methodology is presented for fault diagnosis of a single-variate and dynamic system. A group of wavelet coordinates discriminating the classes of events most efficiently among other wavelet coordinates are determined according to the linear discriminant basis (LDB) method and a principal component analysis (PCA) technique. The proposed feature extractor couples the LDB method with the double wavelet packet tree in order to determine the best configuration of pattern windows causing the most discrimination among classes. The lifting scheme-based wavelet filters are used so that the required computation time is reduced significantly without degrading the robustness of the method. To reduce the size of the feature space, the wavelet coordinates are projected into a new low-dimensional space, by using a PCA technique, where minimum correlation exists among the new space variables. The tuning of some parameters, which affect the performance of the approach, is also discussed. The feature classifier is a binary decision tree that employs a soft-thresholding scheme for recognition of a noisy input pattern. The performance of the proposed technique is examined by a classification benchmark problem, and the faults classification problems for the Tennessee Eastman process. It is observed that the proposed pattern recognition methodology succeeds satisfactorily to classify the noisy input pattern into the known classes of events.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".