State-space-guided neural networks for fault detection
Bibliographic record
Abstract
This article investigates the use of state-space models to enhance neural networks for fault detection in engineering systems. In modern control theory, it is well-established that a nonlinear system can be maintained at a setpoint using a linearized state-space model to approximate system dynamics. This concept is adapted to state-space-guided neural networks (SSGNNs), where a simplified state-space model provides an imperfect approximation of the system state, which is then utilized within a physics-guided neural network (PGNN) framework. By incorporating state-space model estimates into the feature space, the SSGNN can capture intricate patterns and relationships that purely data-driven models might miss. This augmented feature space allows the neural network to learn characteristic relationships between measurements and state-space model estimates, enhancing fault detection capabilities. The methodology emphasizes on guiding a machine learning model with simplified and easily discoverable governing equations while still achieving high fault detection accuracy. This study demonstrates that SSGNNs offer improved fault detection performance compared to benchmark neural networks, using both simulated and laboratory data. These findings encourage further research into hybrid physics-guided machine learning to enhance reliable fault detection in industrial systems. • The study introduces State Space Guided Neural Networks (SSGNNs). • State-space models are used to augment neural network feature space. • These SSGNNs outperform conventional neural networks in fault detection.
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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 | 0.000 |
| 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".