Fault tolerant control strategies for a high-rise building hot water heating system
Bibliographic record
Abstract
Improving energy efficiency and temperature control in hot water heating (HWH) systems are important considerations. The more challenging problem is to maintain good set point control under failure conditions. In this paper, model-based fault detection and diagnosis (FDD) and fault tolerant control (FTC) strategies were designed and simulated for a high-rise building HWH system. An overall system dynamic model with multiple control loops was developed. In the FDD methodology, fuzzy inference systems were employed to isolate the faults and evaluate the fault level. In the FTC strategies, error correcting function was defined consisting of set points, measurement and FDD information. A supply water temperature sensor fault and a multi-fault scenario consisting of heater efficiency fault combined with a partially blocked control valve fault were studied. Simulation runs showed that the developed FDD strategies isolated the faults and the designed FTC strategies were able to improve the system performance. Practical application: The performance of control systems is frequently degraded by the faulty sensors and actuators in hot water heating systems. By implementing the designed fault tolerant control strategies the hot water heating system can be operated to achieve higher energy savings both under fault free and faulty conditions.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".