KaydedilenlerDerginizi ekleyin

Dergiler / International Journal of Applied Mathematics Electronics and Computers / 2020 / Cilt: 8 - Sayı: 4

Submersible Pump Vortex Detection Using Image Processing Technique and Neuro-Fuzzy

Akif DURDU
Nuri ORHAN
Seyit Alperen CELTEK
Muhammet Fatih ASLAN
Kadir SABANCI
Sayfa
163–172
Yayın Tarihi
2020-12-31
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The image processing method has shed light on the studies to be done in the classification of vortexes and the measurement of their strength.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:The vortex means the mass of air or water that spins around very fast that often faced in the agriculture irrigation systems used the pump.
📊
Temel Bulgular & Veri:The undesired effects like loss of hydraulic performance, erosion, vibration and noise may occur because of the vortex in pump systems.
💡
Sonuç & Katkı:The image processing method has shed light on the studies to be done in the classification of vortexes and the measurement of their strength.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Image Processing Technique · Adaptive Neural Fuzzy Network · Vortex Detection

Abstract

The vortex means the mass of air or water that spins around very fast that often faced in the agriculture irrigation systems used the pump. The undesired effects like loss of hydraulic performance, erosion, vibration and noise may occur because of the vortex in pump systems. It is important to detect and prevent vortex for the economic life and efficiency of the agriculture pump. The image processing and neuro-fuzzy based novel model is proposed for the detection of a vortex in the deep well pump used in the agriculture system with this paper. The used images and data -submergence, flow rate, the diameter of the pipe, power consumption, pressure values and noise values- is acquired from an experimental pump. The proposed approach consists of three steps; Neuro-Fuzzy Learning, Image Processing and Neuro-Fuzzy Testing. In the first step, the eighty-two data have employed for the training process of the Neuro-Fuzzy. Then, the images derived from a camera placed near the experimental pump are used to detect vortex in the image processing step. Finally, the relevant data to vortex cases have employed for the testing process of the Neuro-Fuzzy. The result of this study demonstrates that image processing and neuro-fuzzy based design can be successfully used to detect vortex formation. This paper has provided novel contributions in the vortex detection issue such as find out vortex cases by using image processing and Neuro-Fuzzy. The image processing method has shed light on the studies to be done in the classification of vortexes and the measurement of their strength.

🔖

Bu Makaleye Atıf Yap

DURDU, A., ORHAN, N., CELTEK, S. A., ASLAN, M. F., SABANCI, K. (2020). Submersible Pump Vortex Detection Using Image Processing Technique and Neuro-Fuzzy. International Journal of Applied Mathematics Electronics and Computers, Cilt: 8 - Sayı: 4, 163–172. https://ajindex.com/articles/258011

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (8)

  1. T. Nagahara, T. Sato, and T. Okamura, "Effect of the submerged vortex cavitation occurred in pump suction intake on hydraulic forces of mixed flow pump impeller," http://resolver. caltech. edu/cav2001: sessionB8. 006, 2001. F. Gurbuzdal, "Scale effects on the formation of vortices at intake structures," M. Sc. degree, scienc civil engineering, middle east technial University, 2009. B. Hanson, "İrrigation Pumping Plant (UC İrrigation And Drainage Specialist)," University Of California. Davis, 2000. E. C. Nurşen, "Santrifüj Pompalarda Kavitasyon Problemi ve Maksimum Emme Yüksekliği (MEY) Hesabı," presented at the 7. Pompa ve Vana Kongresi, İstanbul, 2011.
  2. O. Konuralp, Ö. Canbaz, and K. Albayrak, "Dünya Dışındaki Gökcisimleri İçin Santrifüj Pompa Seçimi ve Olası Sorunlar," presented at the 8. Pompa Vana Kongresi, İstanbul, 2013.
  3. M. Nasiri, M. Mahjoob, and H. Vahid-Alizadeh, "Vibration signature analysis for detecting cavitation in centrifugal pumps using neural networks," in 2011 IEEE International Conference on Mechatronics, 2011: IEEE, pp. 632-635. H. Karadoğan and N. Ürün, "Pompa Çıkışındaki Basınç Çalkantıları," presented at the 2. Pompa Kongresi, 1996.
  4. E. Çakmak, B. Ünlüer, and H. Karadoğan, "Radyal çark çıkışındaki basınç çalkantıları," presented at the 3. Pompa Kongresi, 1998. M. Čdina, "Detection of cavitation phenomenon in a centrifugal pump using audible sound," Mechanical systems and signal processing, vol. 17, no. 6, pp. 1335-1347, 2003. J. Wang and H. Hu, "Vibration-based fault diagnosis of pump using fuzzy technique," Measurement, vol. 39, no. 2, pp. 176-185, 2006.
  5. N. Sakthivel, V. Sugumaran, and S. Babudevasenapati, "Vibration based fault diagnosis of monoblock centrifugal pump using decision tree," Expert Systems with Applications, vol. 37, no. 6, pp. 4040-4049, 2010.
  6. S. Rajakarunakaran, P. Venkumar, D. Devaraj, and K. S. P. Rao, "Artificial neural network approach for fault detection in rotary system," Applied Soft Computing, vol. 8, no. 1, pp. 740-748, 2008.
  7. N. Sakthivel, V. Sugumaran, and B. B. Nair, "Application of support vector machine (SVM) and proximal support vector machine (PSVM) for fault classification of monoblock centrifugal pump," International Journal of Data Analysis Techniques and Strategies, vol. 2, no. 1, pp. 38-61, 2010.
  8. N. Sakthivel, V. Sugumaran, and B. B. Nair, "Comparison of decision tree-fuzzy and rough set-fuzzy methods for fault categorization of mono-block centrifugal pump," Mechanical systems and signal processing