Time series analysis of field data for soft faults detection and degradation assessment in residential air conditioning systems
Bibliographic record
Abstract
• Field data from residential AC units used to detect soft faults. • Virtual refrigerant charge sensor detects refrigerant leakage. • Temperature difference identifies inadequate condenser airflow. • Analysis of 81 units: 2 had airflow issues, 5 had leakage. • Faults caused 4–26% higher energy use, 4–10% COP reduction. Residential Air Conditioning units are significant contributors to energy consumption. Soft faults in these units, such as refrigerant leakage and inadequate condenser airflow, can lead to reduced equipment life, decreased cooling capacity, and increased energy consumption. While extensive research has been conducted on Fault Detection and Diagnosis (FDD) in AC systems, most studies rely on laboratory-imposed faults or simulations, which may not reflect real-world conditions. Thus, long-term field data analyses remain scarce. This study develops and validates a time-series analysis-based methodology for detecting and diagnosing these faults in residential air conditioning units. Virtual refrigerant charge is used to detect refrigerant leakage, while the difference between condensing and ambient temperatures is used to detect inadequate condenser airflow. The methodology is tested on a dataset of 81 units across the US and Canada, monitored over a full cooling season (2–7 months). Results show that 2 units exhibited degraded condenser airflow and 5 had refrigerant leakage. Refrigerant leakage resulted in a monthly Coefficient of Performance (COP) reduction of 4–10% and an increase in daily energy consumption by 4–26% over a faulty period of 6.5 to 15 weeks. Similarly, units with degraded condenser airflow experienced a COP reduction of 4–7% per month, and daily electricity consumption increased by 15–17% over a faulty period of 8–8.5 weeks. This study quantifies fault performance degradation under residential conditions by analyzing real-world operational data, offering a field-tested approach for identifying and assessing soft faults. This work highlights the importance of timely fault detection and maintenance in residential Air Conditioning units to ensure efficiency, minimize energy waste, and prevent system damage.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".