Fault Detection and Diagnosis of Valve Actuators in Discharge Air Temperature (DAT) Systems, using Interactive Unscented Kalman Filter Estimation
Bibliographic record
Abstract
Nowadays monitoring and controlling the modern and sophisticated heating ventilation air conditioning (HVAC) building systems under a wide variety of occupancy and load related operating conditions is becoming a difficult and challenging task. Their complexity drastically increases and the control becomes more difficult task due to the several control loops that interact between them. Among these control loops the discharge air temperature (DAT) loop, the static pressure loop (SP), and the variable air volume (VAV) terminal unit loop are the candidate loops requiring frequent re-tuning. Equipment failures and loss of control leading to less than acceptable indoor environment conditions is a common problem reported in these systems. In our paper we consider the degradation in the DAT loop performance caused by a gradual increase in backlash of the valve actuator. The main objective of this paper is to describe the application of an interactive multiple model (IMM) based on the unscented Kalman filter (UKF) estimation algorithm (IMMUKF) to the problem of fault detection diagnosis and isolation (FDDI) of the valve actuator failures in DAT loop of the HVAC systems. The proposed algorithm is an alternative to the interactive multiple model (IMM) developed in the literature based on the extended Kalman filter standard technique, the most popular estimation technique used in the last 40 years. The main advantage of the proposed algorithm is the less computation, consequently more faster, high accuracy, robustness and eliminates completely the linearization of the system dynamics
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".