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

With 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.