Online incipient fault diagnosis based on Kullback‐Leibler divergence and recursive principle component analysis
Bibliographic record
Abstract
Abstract Fault detection and isolation (FDI) methods based on the principal component analysis (PCA) model have achieved a large number of theoretical studies and applications, especially for complex and highly dimensional processes. However, the Hotelling's T2, that is the most common used statistical distance, can fail in detecting small shifts such as a sensor incipient fault with low fault‐to‐noise ratio (FNR). Although an incipient fault develops slowly, it cannot be ignored and is necessary to be detected early enough to avoid more serious consequences. In this study, a realistic online diagnosis method for incipient faults with low FNR is presented. Based on probability distribution measure, the Kullback‐Leibler divergence (KLD) is utilized to compare the probability density of each of the latent scores to a reference one. Under the hypothesis of Gaussian distribution, dynamic changes of KLDs are computed via the mean and variance of score vectors, which can be updated online utilizing the recursive principal component analysis (RPCA). From simulations, it is shown that the proposed approach can detect, isolate, and estimate the sensor incipient fault of the multivariate AR system successfully.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".