Fault diagnosis for overcharge and undercharge conditions in refrigeration systems using infrared thermal images

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dc.contributor.authorKatırcıoğlu, Ferzan
dc.contributor.authorCingiz, Zafer
dc.date.accessioned2023-07-26T11:57:58Z
dc.date.available2023-07-26T11:57:58Z
dc.date.issued2023
dc.departmentDÜ, Gölyaka Meslek Yüksekokulu, Elektrik ve Enerji Bölümüen_US
dc.description.abstractWith the increase in industrialization and the number of people and buildings, the need for refrigeration systems has also increased. Maintenance of these systems, malfunctions and their late detection cause time and cost problems. Therefore, in this study, a machine learning application is recommended to diagnose the refrigerant undercharge and refrigerant overcharge faults that may occur in the refrigeration system by using infrared images. Firstly, infrared images obtained from normal charge, undercharge and overcharge situations in the refrigeration system, are passed through the two-dimensional discrete wavelet transform (2D-DWT) and the images are decomposed. Then, statistical texture features from the original input images are obtained by separating infrared images. The dimensionality of the extracted features is reduced using the principal component analysis (PCA) and the ReliefF (RF) algorithm. Finally, these selected features are applied to the K nearest neighbor (k-NN), naive Bayes algorithm (NBA), decision tree (DT), and cascade forward neural network (CFNN) classifiers. It has been found that RF-based feature selection is useful in obtaining the optimal feature vector. The classification results revealed that CFNN outperforms k-NN, NBA, and DT. Compared to traditional electrical measurements and fault detection methods, it has been shown that the recommended system is feasible due to its features such as ease of use, remote measurement, and self-adaptive recognition.en_US
dc.identifier.doi10.1177/09544089221148065
dc.identifier.issn0954-4089
dc.identifier.issn2041-3009
dc.identifier.scopus2-s2.0-85146509215en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.urihttps://doi.org/10.1177/09544089221148065
dc.identifier.urihttps://hdl.handle.net/20.500.12684/13364
dc.identifier.wosWOS:000910664900001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorKatırcıoğlu, Ferzan
dc.institutionauthorCingiz, Zafer
dc.language.isoenen_US
dc.publisherSage Publications Ltden_US
dc.relation.ispartofProceedings of The Institution of Mechanical Engineers Part E-Journal of Process Mechanical Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectFault Diagnosis; Thermal Image Analysis; Machine Learning; Refrigeration Systemen_US
dc.subjectAir-Conditioner; Charge; Performance; Impactsen_US
dc.titleFault diagnosis for overcharge and undercharge conditions in refrigeration systems using infrared thermal imagesen_US
dc.typeArticleen_US

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