A Conditional Invertible Neural Network-Based Fault Detection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Residual generation and hypothesis test are two important components in residual-based fault detection techniques. Recent studies mainly focused on enhancing residual generation algorithms, but often overlook the Gaussian distribution assumption that is required for hypothesis test. Based on the conditional invertible neural network (CINN), this study proposes a novel approach for mapping residual signals into near-Gaussian-distributed latent variables, thereby enhancing the reliability and effectiveness of the hypothesis test for fault detection. With the specially designed architecture using CINN, the proposed mapping from residual signals to latent variables has no information loss, thus guaranteeing the accuracy of the proposed fault detection method. The main contributions of this study are twofold: 1) to ensure that the latent variables have distributions similar to an ideal Gaussian distribution, a novel CINN training approach is proposed and 2) historical process information is incorporated into the residual-to-latent variable mapping, dynamically refining the mapping procedures in response to the system behavior. This approach is primarily used to tackle the challenges posed by nonadditive and non-Gaussian noises in fault detection. A dc speed control system and a wastewater treatment plant are adopted to verify the effectiveness of the proposed fault detection approach.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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 it