Diagnosis of sensor precision degradation using Kullback‐Leibler divergence
Bibliographic record
Abstract
Abstract For practical industrial processes, detection and isolation of a sensor precision degradation fault is of vital importance. In comparison with other sensor fault types such as complete failure and mean shift, the precision degradation fault is usually more difficult to detect and further to isolate. This paper proposes a new fault detection algorithm for sensor precision degradation using Kullback‐Leibler divergence (KLD). The KLD is employed to quantify the dissimilarity between probability densities of each reference score and the actual one within the principal component analysis (PCA) framework. Under the assumption of Gaussian‐distributed data, KLD for each score reduces to a measure of the variance difference, which is proven effective in detecting sensor precision degradation. Control limit of the KLD is established based on historical data, and online fault detection is implemented by adopting a sliding window. The fault isolation issue is also discussed, which aims to determine the faulty sensor with precision degradation. This task may be achieved by checking the PCA loading matrix and matching the fault detection result. Case studies on a synthetic numerical example and the continuous stirred tank reactor (CSTR) process are carried out to demonstrate the effectiveness of the proposed method, in comparison with conventional approaches.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".