Bibliographic record
Abstract
Fault tolerant control (FTC) is essential nowadays in the automation industry. It provides a means for higher equipment availability. Fault in dynamical systems can occur due to the deviation of the system parameters from the normal operating range. Alternatively, it can be a structural change from the normal situation of continuous operation such as the blocking of an actuator due to the mechanical stiction. In this research project, a fault tolerant controller is designed with Matlab Simulink for a feedwater system. The feedwater system components are modified to work under embedded controller design with FTC attached to it. Feedwater systems usually consist of a de-aerator or simply a water storage tank, feedwater pumps, control valves, piping and support fitting elements such as chock valves, anges, hoses and relief valves, beside instrumentation devices like level transmitters, flow transmitters, pressure regulators. The faults are injected separately for each device. Fault diagnostic is used to detect and identify the faults by Limit-checking method. Then a controller is reconfigured to take the action of correcting the hardware failures in the control valve, level sensor, and feedwater pump. The simulation results revealed that the redundant components can take over and handle the process operation when the fault occurs at the duty components. Level sensors are set to work in on-line mode, while the control valves are set to work in off-line mode, due to the mechanical parts movement. Setting the control valves in on-line mode reduces the probability of valve stiction and elongates the component availability. The results reveal the operation of feedwater system is not stopped when a hardware failure takes place in all feedwater system major components. Moreover, the disturbances are not considered in this research as there are many control techniques that can be used to handle the disturbance in a robust way.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".