Abstract
Fault detection in heating, ventilation and air-conditioning (HVAC) systems can effectively prevent equipment damage and system energy loss, and enhance the stability and reliability of system operation. However, existing fault detection strategies have not realized high effectiveness, mainly due to the time-delay characteristics of HVAC system faults and the lack of system-fault operation data. Therefore, aiming at the time delay of system faults and the lack of actual system-fault operation data, this paper proposes a fault detection method that combines a system simulation model and an intelligent detection algorithm. The method first uses the Modelica modeling language to build a scalable simulation model of the system to obtain fault data that are not easily accessible in practice. The long short-term memory-support vector data description (LSTM-SVDD) algorithm is then applied to detect faults in real time by dynamically adjusting the fault residuals according to the absolute difference between the predicted and actual values. The experimental results show that the LSTM-SVDD method improves the average detection accuracy by 9.675% and 9.85% over the classical LSTM network and the extreme gradient boosting (XGBoost) method, respectively, under different fault levels.
Highlights
The modern HVAC systems are complex non-linear systems with large inertia and large hysteresis
Fault Detection Results and Analysis Based on LSTM-SVDD
HVAC systems work with complex processes and delayed fault propagation
Summary
The modern HVAC systems are complex non-linear systems with large inertia and large hysteresis They are often applied in significant locations, such as data centers and communication base stations [1]. Faults in such large-scale complex systems are usually difficult to detect in the initial stages, due to the lack of effective detection techniques, which can lead to excessive energy losses and even significant property damage and social impact. There is, an urgent need to further improve the reliability and safety of HVAC systems in order to reduce the adverse effects caused by faults. Zabala et al [2]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
