Heating, ventilation, and air conditioning (HVAC) systems are a popular research topic because buildings’ energy is mostly used for heating and/or cooling. These systems heavily rely on sensory measurements and typically make an integral part of the smart building concept. As such, they require the implementation of fault detection and diagnosis (FDD) methodologies, which should assist users in maintaining comfort while consuming minimal energy. Despite the fact that FDD approaches are a well-researched subject, not just for improving the operation of HVAC systems but also for a wider range of systems in industrial processes, there is a lack of application in commercial buildings due to their complexity and low transferability. The aim of this review paper is to present and systematize cutting-edge FDD methodologies, encompassing approaches and special techniques that can be applied in HVAC systems, as well as to provide best-practice heuristics for researchers and solution developers in this domain. While the literature analysis targets the FDD perspective, the main focus is put on the data-driven approach, which covers commonly used models and data pre-processing techniques in the field. Data-driven techniques and FDD solutions based on them, which are most commonly used in recent HVAC research, form the backbone of our study, while alternative FDD approaches are also presented and classified to properly contextualize and round out the review.

An effective approach to increasing energy efficiency in buildings without compromising thermal comfort is to optimize heating, ventilation, and air conditioning (HVAC) systems through the use of advanced building-management system features, such as fault detection and diagnosis. Such functions are usually developed based on simulation models that must be calibrated and validated to achieve an appropriate level of accuracy and reliability. The objective of this study was to develop and calibrate a room-level simulation model of a hotel building and its HVAC system using TRNSYS 18 software and real data collected from the smart room system installed in the building. The calibration process was performed with 100 rooms using 5-min samples of room temperatures in selected 1-month periods during the summer and winter seasons by minimizing the root mean squared error (RMSE) in the average of the observed rooms using a genetic algorithm. The calibrated model was able to predict room temperatures with an RMSE of 0.79 ± 0.14 °C and a coefficient of variation in the root mean squared error (cvRMSE) of 3.58 ± 0.7%, which is well below the limits prescribed by international guidelines. The model was then applied to detect faults in the operation of fan coil units in the rooms based on the residual analysis and defined if–then rules. The results obtained show that the model can track the trends of temperature changes in real conditions and successfully detect major anomalies in a system.

The scavenging process is an important part of the two-stroke engine operation. Its efficiency affects the global engine performance such as power, fuel consumption, and pollutant emissions. Slow speed marine diesel engines are uniflow scavenged, which implies inlet scavenging ports on the bottom of the liner and an exhaust valve on the top of the cylinder. A CFD model of such an engine process was developed with the OpenFOAM software tools. A 12-degree sector of the mesh was used corresponding to one of the 30 scavenging ports. A mesh sensitivity test was performed, and the cylinder pressure was compared to experimental data for the analyzed part of the process. The scavenging performances were analyzed for real operation parameters. The influence of the scavenge air pressure and inlet ports geometric orientation was analyzed. The scavenging process is analyzed by means of a passive scalar representing fresh air in the cylinder. Isosurfaces that show the concentration of fresh air were presented. The variation of oxygen and carbon dioxide with time and the axial and angular momentum in the cylinder were calculated. Finally, the scavenging performance for the various operation parameters was evaluated by means of scavenging efficiency, charging efficiency, trapping efficiency, and delivery ratio. It was found that the scavenging efficiency decreases with the engine load due to the shorter time for the process. The scavenging efficiency increases with the pressure difference between the exhaust and scavenging port, and the scavenging efficiency decreases with the increase in the angle of the scavenging ports. It was concluded that smaller angles than the industry standard of 20° could be beneficial to the scavenging efficiency. In the investigation, the charging efficiency ranged from 0.91 to over 0.99, the trapping efficiency ranged from 0.54 to 0.83, the charging efficiency ranged from 0.78 to 0.92, and the delivery ratio ranged from 1.21 to 2.03.

Optimizing the performance of heating, ventilation, and air-conditioning (HVAC) systems is critical in today’s energy-conscious world. Fan coil units (FCUs) play a critical role in providing comfort in various environments as an important component of HVAC systems. However, FCUs often experience failures that affect their efficiency and increase their energy consumption. In this context, deep learning (DL)-based fault detection offers a promising solution. By detecting faults early and preventing system failures, the efficiency of FCUs can be improved. This paper explores DL models as fault detectors for FCUs to enable smarter and more energy-efficient hotel buildings. We tested three contemporary DL modeling approaches: convolutional neural network (CNN), long short-term memory network (LSTM), and a combination of CNN and gated recurrent unit (GRU). The random forest model (RF) was additionally developed as a baseline benchmark. The fault detectors were tested on a real-world dataset obtained from the sensory measurement system installed in a hotel and additionally supplemented with simulated data via a physical model developed in TRNSYS. Three representative FCU faults, namely, a stuck valve, a reduction in airflow, and an FCU outage, were simulated with a much larger dataset than is typically utilized in similar studies. The results showed that the hybrid model, integrating CNN and GRU, performed best for all three observed faults. DL-based fault detectors outperformed the baseline RF model, confirming these solutions as viable components for energy-efficient hotels.