Fault estimation for multi‐rate descriptor systems using bi‐directional long short‐term memory neural network
Bibliographic record
Abstract
Abstract Fault estimation in multi‐rate descriptor systems, which involve both differential and algebraic states, is particularly challenging due to the complexity introduced by multi‐rate measurements. This paper proposes a novel fault estimation approach that combines a differential‐algebraic equation based extended Kalman filter (DAE‐EKF) with a bi‐directional long short‐term memory (bi‐LSTM) neural network. The DAE‐EKF is used to generate multi‐rate residuals, which serve as inputs to neural networks to estimate faults. bi‐LSTM networks improve upon LSTMs by processing data in both forward and backward directions, using past and future information. This bidirectional approach enhances temporal dependency capture, making bi‐LSTMs ideal for accurate fault estimation. The efficacy of the proposed method is demonstrated using simulation studies on a two‐phase reactor‐condenser system with recycle and a reactive distillation system. The proposed approach has shown superior fault estimation capability for multi‐rate descriptor systems compared to DAE‐EKF with conventional feedforward neural networks and DAE‐EKF with LSTM.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".