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

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.

Keywords

• Image Processing Technique
• Adaptive Neural Fuzzy Network
• Vortex Detection
• Submergence

References

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.
2. F. Gurbuzdal, "Scale effects on the formation of vortices at intake structures," M. Sc. degree, scienc civil engineering, middle east technial University, 2009.
3. B. Hanson, "İrrigation Pumping Plant (UC İrrigation And Drainage Specialist)," University Of California. Davis, 2000.
4. E. C. Nurşen, "Santrifüj Pompalarda Kavitasyon Problemi ve Maksimum Emme Yüksekliği (MEY) Hesabı," presented at the 7. Pompa ve Vana Kongresi, İstanbul, 2011.
5. 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.
6. 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.
7. H. Karadoğan and N. Ürün, "Pompa Çıkışındaki Basınç Çalkantıları," presented at the 2. Pompa Kongresi, 1996.
8. E. Çakmak, B. Ünlüer, and H. Karadoğan, "Radyal çark çıkışındaki basınç çalkantıları," presented at the 3. Pompa Kongresi, 1998.

9. 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.
10. J. Wang and H. Hu, "Vibration-based fault diagnosis of pump using fuzzy technique," Measurement, vol. 39, no. 2, pp. 176-185, 2006.
11. 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.
12. 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.
13. 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.
14. 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, vol. 24, no. 6, pp. 1887-1906, 2010.
15. N. Sakthivel, B. B. Nair, V. Sugumaran, and R. S. Rai, "Decision support system using artificial immune recognition system for fault classification of centrifugal pump," International Journal of Data Analysis Techniques and Strategies, vol. 3, no. 1, pp. 66-84, 2011.
16. H. Wang and P. Chen, "Fault diagnosis of centrifugal pump using symptom parameters in frequency domain," Agricultural Engineering International: CIGR Journal, 2007.
17. J. Rafiee, F. Arvani, A. Harifi, and M. Sadeghi, "Intelligent condition monitoring of a gearbox using artificial neural network," Mechanical systems and signal processing, vol. 21, no. 4, pp. 1746-1754, 2007.
18. B.-S. Yang, M.-S. Oh, and A. C. C. Tan, "Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference," Expert Systems with Applications, vol. 36, no. 2, pp. 1840-1849, 2009.
19. J.-S. Jang, "Input selection for ANFIS learning," in Proceedings of IEEE 5th International Fuzzy Systems, 1996, vol. 2: IEEE, pp. 1493-1499.
20. J.-S. Jang, "ANFIS: adaptive-network-based fuzzy inference system," IEEE transactions on systems, man, and cybernetics, vol. 23, no. 3, pp. 665-685, 1993.
21. H. Atmaca, B. Cetisli, and H. S. Yavuz, "The comparison of fuzzy inference systems and neural network approaches with ANFIS method for fuel consumption data," in Second International Conference on Electrical and Electronics Engineering Papers ELECO, 2001: Citeseer.
22. E. Avci, "Comparison of wavelet families for texture classification by using wavelet packet entropy adaptive network based fuzzy inference system," Applied Soft Computing, vol. 8, no. 1, pp. 225-231, 2008.
23. E. Avci, I. Turkoglu, and M. Poyraz, "Intelligent target recognition based on wavelet packet neural network," Expert Systems with Applications, vol. 29, no. 1, pp. 175-182, 2005.
24. E. Avci and Z. H. Akpolat, "Speech recognition using a wavelet packet adaptive network based fuzzy inference system," Expert Systems with Applications, vol. 31, no. 3, pp. 495-503, 2006.
25. M. A. Boyacioglu and D. Avci, "An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: the case of the Istanbul stock exchange," Expert Systems with Applications, vol. 37, no. 12, pp. 7908-7912, 2010.
26. O. Demirel, A. Kakilli, and M. Tektas, "Electric energy load forecasting using ANFIS and ARMA methods," 2010.
27. K. Guney and N. Sarikaya, "Adaptive neuro-fuzzy inference system for computing the resonant frequency of electrically thin and thick rectangular microstrip antennas," International Journal of Electronics, vol. 94, no. 9, pp. 833-844, 2007.
28. K. Kumaş, "Binalarda ısıtma yükü ihtiyacının belirlenmesi için yeni bir yaklaşım," Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü, 2014.
29. M. Caner and E. Akarslan, "Estimation of specific energy factor in marble cutting process using ANFIS and ANN," Pamukkale University Journal of Engineering Sciences, vol. 15, no. 2, pp. 221-226, 2009.
30. S. Ya-Lin and B. Chen-Xi, "Research and analysis of image processing technologies based on dotnet framework," Physics Procedia, vol. 25, pp. 2131-2137, 2012.
31. S. Ojha and S. Sakhare, "Image processing techniques for object tracking in video surveillance-A survey," in 2015 International Conference on Pervasive Computing (ICPC), 2015: IEEE, pp. 1-6.
32. F. Yan, A. M. Iliyasu, and P. Q. Le, "Quantum image processing: a review of advances in its security technologies," International Journal of Quantum Information, vol. 15, no. 03, p. 1730001, 2017.
33. A. P. James and B. V. Dasarathy, "Medical image fusion: A survey of the state of the art," Information fusion, vol. 19, pp. 4-19, 2014.
34. M. F. Aslan, M. Ceylan, and A. Durdu, "Segmentation of Retinal Blood Vessel Using Gabor Filter and Extreme Learning Machines," in 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), 2018: IEEE, pp. 1-5.
35. I. Makki, R. Younes, C. Francis, T. Bianchi, and M. Zucchetti, "A survey of landmine detection using hyperspectral imaging," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 124, pp. 40-53, 2017.
36. K. Sabanci, A. Toktas, and A. Kayabasi, "Grain classifier with computer vision using adaptive neuro‐fuzzy inference system," Journal of the Science of Food and Agriculture, vol. 97, no. 12, pp. 3994-4000, 2017.
37. K. Sabanci and C. Aydin, "Smart robotic weed control system for sugar beet," 2018.
38. M. F. Aslan, A. Durdu, and K. Sabanci, "Shopping Robot That Make Real Time Color Tracking Using Image Processing Techniques," International Journal of Applied Mathematics Electronics and Computers, vol. 5, no. 3, pp. 62-66, 2017.
39. F. Jalled and I. Voronkov, "Object detection using image processing," arXiv preprint arXiv:1611.07791, 2016.
40. R. Verschae and J. Ruiz-del-Solar, "Object detection: current and future directions," Frontiers in Robotics and AI, vol. 2, p. 29, 2015.