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Must read in IJR: a comprehensive review on fault detection in HVAC systems

The latest research and developments in the fault detection and diagnostics of HVAC systems are presented in several articles recently published in the IJR.

According to recent research articles, detection and diagnosis of faults in HVAC systems result in significant energy savings and help improve thermal comfort in buildings. Indeed, energy losses due to faulty operation of HVAC components account for around 15–30% of the energy used in the buildings. [1] For example, in unitary air conditioner equipment, common faults include improper refrigerant charge, compressor valve leak, liquid line restriction, low evaporator airflow, low condenser airflow, evaporator and condenser fouling, and non-condensable gasses. [1]

 

In a review article published in IIR’s International Journal of Refrigeration, Singh et al. describe the basics of fault detection and diagnostics (FDD) in HVAC systems, along with the methods developed for FDD. [1] The authors underline the fact that machine learning methods have become prevalent in FDD. They also present the role of IoT and cloud technology in FDD. Finally, they discuss fault modelling, which is necessary to test and validate FDD algorithms.

 

In the refrigeration industry, good performance of a fault detection algorithm can be defined as high classification accuracy, low computation time, and low false positive rate. High classification accuracy ensures an accurate fault description to technicians for quick troubleshooting, while low computation time is important as it lowers detection time and hardware cost. A low false positive rate increases the reliability of the fault detection model and results in lower expenses regarding service call rate. Therefore, it is essential to evaluate FDD algorithms based on these factors. [2] In another article, Soltani et al compare different machine learning classifiers to find the best solution for diagnosing twenty faults possibly encountered in industrial refrigeration systems. [2]

 

 

Find out more by downloading these articles in FRIDOC.
A Comprehensive Review: Fault Detection, Diagnostics, Prognostics, and Fault Modelling in HVAC Systems
Fault detection and diagnosis in refrigeration systems using machine learning algorithms

 

 

Sources

[1] Singh, V., Mathur, J., & Bhatia, A. (2022). A Comprehensive Review: Fault Detection, Diagnostics, Prognostics, and Fault Modelling in HVAC Systems. International Journal of Refrigeration. https://doi.org/10.1016/j.ijrefrig.2022.08.017

[2] Soltani, Z., Sørensen, K. K., Leth, J., & Bendtsen, J. D. (2022). Fault detection and diagnosis in refrigeration systems using machine learning algorithms. International Journal of Refrigeration https://doi.org/10.1016/j.ijrefrig.2022.08.008