Design of integrated fault detection, diagnosis and reconfigurable control systems
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
In this paper, a novel approach for integrated fault detection, diagnosis and reconfigurable control systems design in the discrete-time stochastic domain is proposed. The scheme is based on a two-stage adaptive Kalman filter for simultaneous state and fault parameter estimation, statistical decisions for fault detection, diagnosis and activation of controller reconfiguration. Using the information provided by the fault detection and diagnosis scheme, the reconfigurable controller is designed automatically using an eigenstructure assignment technique. To eliminate the steady-state tracking error, a feedforward reconfigurable control law is also incorporated. The proposed approach has been evaluated using two examples. The effectiveness and superiority of the proposed approach have been demonstrated in comparison with existing reconfigurable controllers which are based on linear quadratic regulator and other eigenstructure assignment techniques.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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