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Soğutma Sistemlerindeki Soğutucu Akışkan Kaçak Tespiti İçin Kızılötesi Görüntüler Üzerinde Pearson Korelasyon Benzerlik Analiz Yönteminin Kullanılması

Yıl 2020, Ejosat Özel Sayı 2020 (ISMSIT), 28 - 36, 30.11.2020
https://doi.org/10.31590/ejosat.818440

Öz

Soğutma sistemlerinde soğutucu akışkan kaçakları montaj ve servis hatalarından kaynaklanabildiği gibi, zaman içinde boru ve bağlantılardaki titreşime ve malzeme yıpranmasına bağlı olarak oluşabilmektedir. Bu durum, soğutma sisteminin çalışma verimini engelleyerek, soğutma/ısıtma kapasitesini düşürme ve sistemde kızgınlık artışına neden olmaktadır. Ayrıca, emme hattında vakum düşüşü gerçekleşeceği için sisteme hava ve nem girişinden dolayı iç kirlenme ve tıkanma oluşmaktadır. Soğutucu akışkan kaçaklarının tespitinde köpükleme ve akışkan kaçak dedektörü ile algılama kullanılan en yaygın yöntemlerdir. Bu çalışmada, bir soğutma sisteminde oluşabilecek kaçakların tespiti için, kızılötesi görüntü işleme tekniğine dayalı yeni bir yöntem önerilmiştir. Bunun için ilk olarak, R22 soğutucu akışkan kullanılarak hazırlanan deney düzeneğinde, 7 farklı noktada, farklı zamanlarda yapay kaçaklar oluşturulur. Daha sonra, sağlam sistem kızılötesi görüntüsü ile yapay kaçak yapılmış sistem görüntüleri üzerinden daha önceden tespit edilmiş 12 alt bölgeden öznitelik verileri çıkartılmaktadır. Öznitelik özellik verilerinin elde edilme işleminde, belirlenen 12 bölgenin yüzey sıcaklık bilgileri kullanılmıştır. Yüzey sıcaklık bilgileri minimum, maksimum, ortalama ve en yoğun sıcaklık bilgisi olarak dört farklı şekilde çeşitlendirilmiştir. Kızılötesi görüntü analizi işlemini gerçekleştirmek için elde edilen öznitelik verileri Pearson Korelasyon Benzerlik Analizi (PKBA) işlemine sokulmaktadır. Son olarak, izlenmesi gereken 12 alt bölgenin PKBA sonuçları bir eşik değere tabi tutularak, eşik değerin altında kalan bölgelerde “Kaçak vardır” tespiti yapılmaktadır. Diğer bir olası durum ise, eğer sağlam ve kaçak kızılötesi karşılaştırmasında, benzerlik değerinin çok yüksek bir değer oluşmasında, bu bölgeler için “Kaçak yoktur” sonucunu vermektedir. Kızılötesi görüntü işleme tekniği kullanılarak gerçekleştirilen PKBA ile kaçak tespit işlemi, kullanım kolaylığı, hızlı ve eş zamanlı kaçak tespit etme noktasında daha avantajlı olduğunu göstermiştir.

Destekleyen Kurum

TÜBİTAK

Proje Numarası

218M936

Kaynakça

  • Ahlgren, P., Jarneving, B., & Rousseau, R. (2003). Requirements for a cocitation similarity measure, with special reference to Pearson's correlation coefficient. Journal of the American Society for Information Science and Technology, 54(6), 550-560.
  • Braun, J. E. (2003). Automated fault detection and diagnostics for vapor compression cooling equipment. J. Sol. Energy Eng., 125(3), 266-274.
  • Corberán, J. M.-G.-B.-M.-P. (2011). Influence of the source and sink temperatures on the optimal refrigerant charge of a water-to-water heat pump. International Journal of Refrigeration, 34(4), 881-892.
  • Francis, C. M. (2017). An investigation of refrigerant leakage in commercial refrigeration. International Journal of Refrigeration, 74, 12-21.
  • Goswami, D. Y. (2001). Effect of refrigerant charge on the performance of air conditioning systems. International journal of energy research, 25(8), 741-750.
  • Grace, I. D. (2005). Sensitivity of refrigeration system performance to charge levels and parameters for online leak detection. Appl. Therm. Eng., 25(4), 557-566.
  • Katırcıoğlu, F. Ç. (2019). Infrared image enhancement model based on gravitational force and lateral inhibition networks. Infrared Physics & Technology, 100, 15-27.
  • Kocyigit, N. B. (2014). Fault diagnosis of a vapor compression refrigeration system with hermetic reciprocating compressor based on ph diagram. International journal of refrigeration, 45, 44-54.
  • Koronaki, I. P. (2012). Refrigerant emissions and leakage prevention across Europe–Results from the RealSkillsEurope project. Energy, 45(1), 71-80.
  • Linde. (2020). Linde gas. Mayıs 26, 2020 tarihinde www.linde-gas.com: https://www.linde-gas.com /en/products_and_supply/refrigerants/hcfc_refrigerants/r22/index.html.
  • Llopis, R., Cabello, R., Sánchez, D., Torrella, E., Patiño, J., & Sánchez, J. G. (2011). Experimental evaluation of HCFC-22 replacement by the drop-in fluids HFC-422A and HFC-417B for low temperature refrigeration applications. Applied Thermal Engineering, 31(6-7), 1323-1331.
  • Madani, H. &. (2014). A comprehensive study on the important faults in heat pump system during the warranty period. International journal of refrigeration, 48, 19-25.
  • Mota-Babiloni, A. N.-E.-C. (2015). Commercial refrigeration–an overview of current status. International journal of refrigeration, 57, 186-196.
  • Poggi, F. M.-T. (2008). Refrigerant charge in refrigerating systems and strategies of charge reduction. International Journal of Refrigeration, 31(3), 353-370.
  • Ramschie, A. A. (2017). Method of Freon Leak Detection and Dirty Air Filter in Air Conditioning for Electrical Savings. International Journal of Computer Applications, 172(1), 35-40.
  • Rossi, T. M. (2004). Unitary air conditioner field performance.
  • Takeuchi, S. &. (2018). Fault Diagnosis Method Based on Scaling Law for On-line Refrigerant Leak Detection. I. 2. (ICMLA), 1087-1094.
  • Tassou, S. A. (2005). Fault diagnosis and refrigerant leak detection in vapour compression refrigeration systems. . International Journal of Refrigeration, 28(5), 680-688.
  • Union, O. J. (2007). Commission Regulation (EC) No 1516/2007, no. L335/10. Brussels.
  • Yoo, J. W. (2017). Refrigerant leakage detection in an EEV installed residential air conditioner with limited sensor installations. International Journal of Refrigeration, 78, 157-165.

Using Pearson Correlation Similarity Analysis Method on Infrared Images to Detect Refrigerant Leakage in Refrigeration Systems

Yıl 2020, Ejosat Özel Sayı 2020 (ISMSIT), 28 - 36, 30.11.2020
https://doi.org/10.31590/ejosat.818440

Öz

Refrigerant leaks in refrigeration systems can be caused by assembly and service failures, as well as due to vibration and material wear in pipes and connections over time. This situation prevents the working efficiency of the refrigeration system, decreasing the cooling/heating capacity and causing an increase in superheating in the system. In addition, internal contamination and clogging occurs due to air and humidity entry into the system, as there will be a vacuum drop in the suction line. Foaming and detection with fluid leakage detector are the most common methods used to detect refrigerant leakages. In this study, a new method based on infrared image processing technique was proposed to detect leakages that may occur in a refrigeration system. For this, firstly, in the experimental setup prepared using R22 refrigerant, artificial leakages are created at 7 different points and at different times. Afterwards, feature data are extracted from 12 sub-regions previously determined by using the robust system infrared image and artificially illegal system images. In the process of obtaining feature property data, the surface temperature information of 12 sub-regions determined was used. Surface temperature information has been diversified in four different ways as minimum, maximum, average and most intense temperature information. In order to perform the infrared image analysis process, the feature data obtained are entered into the Pearson Correlation Similarity Analysis (PCSA) process. Finally, the PCSA results of the 12 sub-regions that need to be monitored are subjected to a threshold value and the regions below the threshold value are detected as “There is leakage”. Another possible situation is that if the similarity value is very high in the comparison of robust and artificially illegal system infrared, it gives the result "No leakage" for these regions. Leakage detection with PCSA using infrared image processing technique has shown that it is more advantageous in terms of ease of use, fast and simultaneous leakage detection.

Proje Numarası

218M936

Kaynakça

  • Ahlgren, P., Jarneving, B., & Rousseau, R. (2003). Requirements for a cocitation similarity measure, with special reference to Pearson's correlation coefficient. Journal of the American Society for Information Science and Technology, 54(6), 550-560.
  • Braun, J. E. (2003). Automated fault detection and diagnostics for vapor compression cooling equipment. J. Sol. Energy Eng., 125(3), 266-274.
  • Corberán, J. M.-G.-B.-M.-P. (2011). Influence of the source and sink temperatures on the optimal refrigerant charge of a water-to-water heat pump. International Journal of Refrigeration, 34(4), 881-892.
  • Francis, C. M. (2017). An investigation of refrigerant leakage in commercial refrigeration. International Journal of Refrigeration, 74, 12-21.
  • Goswami, D. Y. (2001). Effect of refrigerant charge on the performance of air conditioning systems. International journal of energy research, 25(8), 741-750.
  • Grace, I. D. (2005). Sensitivity of refrigeration system performance to charge levels and parameters for online leak detection. Appl. Therm. Eng., 25(4), 557-566.
  • Katırcıoğlu, F. Ç. (2019). Infrared image enhancement model based on gravitational force and lateral inhibition networks. Infrared Physics & Technology, 100, 15-27.
  • Kocyigit, N. B. (2014). Fault diagnosis of a vapor compression refrigeration system with hermetic reciprocating compressor based on ph diagram. International journal of refrigeration, 45, 44-54.
  • Koronaki, I. P. (2012). Refrigerant emissions and leakage prevention across Europe–Results from the RealSkillsEurope project. Energy, 45(1), 71-80.
  • Linde. (2020). Linde gas. Mayıs 26, 2020 tarihinde www.linde-gas.com: https://www.linde-gas.com /en/products_and_supply/refrigerants/hcfc_refrigerants/r22/index.html.
  • Llopis, R., Cabello, R., Sánchez, D., Torrella, E., Patiño, J., & Sánchez, J. G. (2011). Experimental evaluation of HCFC-22 replacement by the drop-in fluids HFC-422A and HFC-417B for low temperature refrigeration applications. Applied Thermal Engineering, 31(6-7), 1323-1331.
  • Madani, H. &. (2014). A comprehensive study on the important faults in heat pump system during the warranty period. International journal of refrigeration, 48, 19-25.
  • Mota-Babiloni, A. N.-E.-C. (2015). Commercial refrigeration–an overview of current status. International journal of refrigeration, 57, 186-196.
  • Poggi, F. M.-T. (2008). Refrigerant charge in refrigerating systems and strategies of charge reduction. International Journal of Refrigeration, 31(3), 353-370.
  • Ramschie, A. A. (2017). Method of Freon Leak Detection and Dirty Air Filter in Air Conditioning for Electrical Savings. International Journal of Computer Applications, 172(1), 35-40.
  • Rossi, T. M. (2004). Unitary air conditioner field performance.
  • Takeuchi, S. &. (2018). Fault Diagnosis Method Based on Scaling Law for On-line Refrigerant Leak Detection. I. 2. (ICMLA), 1087-1094.
  • Tassou, S. A. (2005). Fault diagnosis and refrigerant leak detection in vapour compression refrigeration systems. . International Journal of Refrigeration, 28(5), 680-688.
  • Union, O. J. (2007). Commission Regulation (EC) No 1516/2007, no. L335/10. Brussels.
  • Yoo, J. W. (2017). Refrigerant leakage detection in an EEV installed residential air conditioner with limited sensor installations. International Journal of Refrigeration, 78, 157-165.
Toplam 20 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Ferzan Katırcıoğlu 0000-0001-5463-3792

Zafer Cingiz 0000-0003-3796-755X

Yusuf Çay 0000-0003-4007-6168

Ali Gürel 0000-0003-1430-8041

Ahmet Kolip 0000-0001-6666-1141

Proje Numarası 218M936
Yayımlanma Tarihi 30 Kasım 2020
Yayımlandığı Sayı Yıl 2020 Ejosat Özel Sayı 2020 (ISMSIT)

Kaynak Göster

APA Katırcıoğlu, F., Cingiz, Z., Çay, Y., Gürel, A., vd. (2020). Soğutma Sistemlerindeki Soğutucu Akışkan Kaçak Tespiti İçin Kızılötesi Görüntüler Üzerinde Pearson Korelasyon Benzerlik Analiz Yönteminin Kullanılması. Avrupa Bilim Ve Teknoloji Dergisi28-36. https://doi.org/10.31590/ejosat.818440