An interacting multiple-model based fault detection, diagnosis and fault-tolerant control approach
Bibliographic record
Abstract
In this paper, an interacting multiple-model (IMM) based fault detection, diagnosis and reconfigurable control approach for discrete-time stochastic systems is proposed. Fault detection and diagnosis (FDD) is carried out using the IMM estimator. The linear quadratic regulator (LQR) and an eigenstructure assignment (EA) techniques have been used for nominal and reconfigurable control laws design, respectively. To achieve zero steady-state tracking error, a set of feedforward control gains is also designed using an input weighting approach. The paper has considered not only actuator and sensor faults, but also system component faults. To achieve fast and reliable fault detection, diagnosis and controller reconfiguration, new fault diagnosis and reconfiguration mechanisms have been proposed using appropriate combination of the information provided by the mode probabilities from the IMM algorithm and an index related to the closed-loop system performance. The proposed approach is evaluated using an aircraft example in the presence of system component, actuator and sensor faults.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".