Detection of Vortex Cavitation With The Method Adaptive Neural Fuzzy Networks in the Deep Well Pumps
Öz
Nowadays submersible deep well pumps are the most used irrigation systems in agriculture field. Efficient operation and economical life of pumps is an important issue. One of the most important parameters affecting pump efficiency and life is cavitation The cavitation is one of the problems frequently faced in the pump systems that widely used in the agriculture field. The cavitation could cause more undesired effects such as loss of hydraulic performance, erosion, vibration and noise. This paper presents a novel model for the detection of vortex cavitation in the deep well pump used in the agriculture system using adaptive neural fuzzy networks. The data submergence, flow rate, power consumption, pressure values, and noise values used for training the ANFIS (Adaptive-Network Based Fuzzy Inference Systems) network are acquired from an experimental pump. In this study, we use to the sixty-seven data for training process, while the fifteen data have used for testing of our model. The average percentage error (APE) has obtained as 0.08 % and as 0.34 % respectively for 67 training data and for 15 test data. The performance of the implemented model shows the advantages of ANFIS. The result of this study shows that ANFIS can be successfully used to detect vortex cavitation. This paper has two novel contributions which are the usage of noise value on cavitation detection and find out cavitation by using adaptive neural fuzzy networks. During the cavitation, the pump parameters must change by controller for prevent unwanted pump errors. The strategy proposed could be preliminary study of automatic pump control. Also proposed novel control strategy can be used for cavitation control in agriculture irrigation pumps, because of easy set up and no need extra cost. The ANFIS based model has real-time applicable thanks to rapid and easy control. It is possible to set safe boundaries in submergence in this model. Thus, users by adjusting controllable parameters can prevent cavitation and increase pump efficiency.
Anahtar Kelimeler
Adaptive fuzzy neural networks Cavitation Submergence Vortex cavitation Deep well pumps
Destekleyen Kurum
TUBİTAK
Proje Numarası
213O140
Teşekkür
This study was supported by The Scientific and Technical Research Council of Turkey (TUBITAK, Project No:213O140). The authors would also like to thank the Karamanoglu Mehmetbey University for providing the access MATLAB Software and Prof. Dr. Sedat Calisir.
Kaynakça
- Albayrak, K., Konuralp, O., & Canbaz, Ö. (2013). Dünya Dışındaki Gökcisimleri İçin Santrifüj Pompa Seçimi ve Olasi Sorunlar. Paper presented at the 8. Pompa ve Vana Kongresi, İstanbul.
- Atmaca, H., Cetisli, B., & Yavuz, H. S. (2001). The comparison of fuzzy inference systems and neural network approaches with ANFIS method for fuel consumption data. Paper presented at the Second International Conference on Electrical and Electronics Engineering Papers ELECO.
- Avci, E., & Akpolat, Z. H. (2006). Speech recognition using a wavelet packet adaptive network based fuzzy inference system. Expert Systems with Applications, 31(3), 495-503.
- Avci, E., Turkoglu, I., & Poyraz, M. (2005). Intelligent target recognition based on wavelet adaptive network based fuzzy inference system. Paper presented at the Iberian Conference on Pattern Recognition and Image Analysis.
- Boyacioglu, M. A., & Avci, D. (2010). 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, 37(12), 7908-7912.
- Caner, M., & Akarslan, E. (2009). Estimation of specific energy factor in marble cutting process using ANFIS and ANN. Pamukkale University Journal of Engineering Sciences, 15(2), 221-226.
- Čdina, M. (2003). Detection of cavitation phenomenon in a centrifugal pump using audible sound. Mechanical systems and signal processing, 17(6), 1335-1347.
- Demirel, O., Kakilli, A., & Tektas, M. (2010). Electric energy load forecasting using ANFIS and ARMA methods.
- Guney, K., & Sarikaya, N. (2007). Adaptive neuro-fuzzy inference system for computing the resonant frequency of electrically thin and thick rectangular microstrip antennas. International Journal of Electronics, 94(9), 833-844. Gurbuzdal, F. (2009). Scale effects on the formation of vortices at intake structures. M. Sc. degree, scienc civil engineering, middle east technial University.
- Hanson, B. (2000). Irrigation Pumping Plants Retrieved from UC Irrigation and Drainage Specialist:
- Jang, J.-S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685.
- Jang, J. (1996). Input selection for ANFIS learning, fuzzy systems. Proceedings of IEEE 5th International Fuzzy Systems. v2, 1493-1499.
- Karadoğan, H., & Ürün, N. (1996). Pompa Çıkışında Basınç Dalgalanmaları. Paper presented at the 2. Pompa Kongresi, İstanbul.
- Kumaş, K. (2014). Binalarda ısıtma yükü ihtiyacının belirlenmesi için yeni bir yaklaşım. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü,
- Nagahara, T., Sato, T., & Okamura, T. (2001). 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.
- Nasiri, M., Mahjoob, M., & Vahid-Alizadeh, H. (2011). Vibration signature analysis for detecting cavitation in centrifugal pumps using neural networks. Paper presented at the 2011 IEEE International Conference on Mechatronics.
- Nurşen, E. C. (2011). Santrifüj Pompalarda Kavitasyon Problemi ve Maksimum Emme Yüksekliği (MEY) Hesabı Paper presented at the 7. Pompa ve Vana Kongresi İstanbul.
- Rafiee, J., Arvani, F., Harifi, A., & Sadeghi, M. (2007). Intelligent condition monitoring of a gearbox using artificial neural network. Mechanical systems and signal processing, 21(4), 1746-1754.
- Rajakarunakaran, S., Venkumar, P., Devaraj, D., & Rao, K. S. P. (2008). Artificial neural network approach for fault detection in rotary system. Applied Soft Computing, 8(1), 740-748.
- Sakthivel, N., Nair, B. B., Sugumaran, V., & Rai, R. S. (2011). Decision support system using artificial immune recognition system for fault classification of centrifugal pump. International Journal of Data Analysis Techniques and Strategies, 3(1), 66-84.
- Sakthivel, N., Sugumaran, V., & Babudevasenapati, S. (2010). Vibration based fault diagnosis of monoblock centrifugal pump using decision tree. Expert Systems with Applications, 37(6), 4040-4049.
- Sakthivel, N., Sugumaran, V., & Nair, B. B. (2010a). 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, 2(1), 38-61.
- Sakthivel, N., Sugumaran, V., & Nair, B. B. (2010b). Comparison of decision tree-fuzzy and rough set-fuzzy methods for fault categorization of mono-block centrifugal pump. Mechanical systems and signal processing, 24(6), 1887-1906.
- Wang, H., & Chen, P. (2007). Fault diagnosis of centrifugal pump using symptom parameters in frequency domain. Agricultural Engineering International: CIGR Journal. Wang, J., & Hu, H. (2006). Vibration-based fault diagnosis of pump using fuzzy technique. Measurement, 39(2), 176-185.
- Yang, B.-S., Oh, M.-S., & Tan, A. C. C. (2009). Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference. Expert Systems with Applications, 36(2), 1840-1849.
- Yüksel, E. and Eker, B., (2009a). Determination of Possible Wear on the Centrifugal Pump Wheel Used for Agricultural Irrigation Purposes. Journal of Tekirdag Agricultural Faculty, 6(2): 203-214.
- Yüksel, E. and Eker, B., (2009b). Determination of Wear That Can be Formed at the Stainless-Steel Wheels of the Centrifuge Pumps Used at Agricultural Irrigation. Journal of Tekirdag Agricultural Faculty, 2009; 6(3): 303-314.
Detection of Vortex Cavitation With The Method Adaptive Neural Fuzzy Networks in the Deep Well Pumps
Öz
Günümüzde tarım alanlarının sulanma işlemlerinde en çok dalgıç derin kuyu pompaları kullanılmaktadır. Pompaların verimli çalışması ve ekonomik ömrü önemli bir konudur. Pompa verimlerini ve ömrünü etkileyen en önemli parametrelerden biri kavitasyondur. Kavitaasyon tarım alanında yaygın olarak kullanılan pompa sistemlerinde sıklıkla karşılaşılan sorunlardan biridir. Kavitasyon, hidrolik performans kaybı, erozyon, titreşim ve gürültü gibi daha fazla istenmeyen etkilere neden olabilir. Bu makale, uyarlanabilir sinirsel bulanık ağları kullanarak tarım sisteminde kullanılan derin kuyu pompasında girdap kavitasyonunun tespiti için yeni bir model sunmaktadır. ANFIS (Uyarlanabilir Ağ Tabanlı Bulanık Çıkarım Sistemleri) ağını eğitmek için kullanılan dalma derinliği, debi, güç tüketimi, basınç değerleri ve gürültü değerleri deneysel bir pompadan elde edilmiştir. Bu çalışmada, eğitim süreci için altmış yedi veriyi kullanırken, on beş veri modelimizi test etmek için kullanılmıştır. Ortalama yüzde hata (APE) 67 eğitim verisi ve 15 test verisi için sırasıyla%0.08 ve% 0.34 olarak elde edilmiştir. Uygulanan modelin performansı ANFIS'in avantajlarını göstermektedir. Bu çalışmanın sonucu, ANFIS'in girdap kavitasyonunu tespit etmek için başarıyla kullanılabileceğini göstermektedir. Bu çalışmanın iki yeni katkısı kavitasyon tespitinde gürültü seviye değişiminin kullanımı ve uyarlanabilir sinirsel bulanık ağları kullanarak kavitasyonun belirlenmesi olmuştur. Kavitasyon sırasında, istenmeyen pompa hatalarını önlemek için pompa parametreleri kontrolör tarafından değiştirilmelidir. Önerilen strateji, otomatik pompa kontrolünün ön çalışması olabilir. Ayrıca önerilen yeni kontrol stratejisi, kurulumunun kolay olması ve ekstra maliyet gerektirmemesi nedeniyle tarımsal sulama pompalarında kavitasyon kontrolü için kullanılabilir. ANFIS tabanlı model, hızlı ve kolay kontrol sayesinde gerçek zamanlı uygulanabilirliğe sahiptir. Bu modelde dalma derinliğinin güvenli sınırları ortaya koymak mümkündür. Böylece kullanıcılar kontrol edilebilir parametreleri ayarlayarak kavitasyonu önleyebilir ve pompa verimini artırabilir.
Anahtar Kelimeler
Uyarlanabilir bulanık sinir ağları Kavitasyon Dalma derinliği Vorteks kavitasyonu Derin kuyu pompaları
Proje Numarası
213O140
Kaynakça
- Albayrak, K., Konuralp, O., & Canbaz, Ö. (2013). Dünya Dışındaki Gökcisimleri İçin Santrifüj Pompa Seçimi ve Olasi Sorunlar. Paper presented at the 8. Pompa ve Vana Kongresi, İstanbul.
- Atmaca, H., Cetisli, B., & Yavuz, H. S. (2001). The comparison of fuzzy inference systems and neural network approaches with ANFIS method for fuel consumption data. Paper presented at the Second International Conference on Electrical and Electronics Engineering Papers ELECO.
- Avci, E., & Akpolat, Z. H. (2006). Speech recognition using a wavelet packet adaptive network based fuzzy inference system. Expert Systems with Applications, 31(3), 495-503.
- Avci, E., Turkoglu, I., & Poyraz, M. (2005). Intelligent target recognition based on wavelet adaptive network based fuzzy inference system. Paper presented at the Iberian Conference on Pattern Recognition and Image Analysis.
- Boyacioglu, M. A., & Avci, D. (2010). 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, 37(12), 7908-7912.
- Caner, M., & Akarslan, E. (2009). Estimation of specific energy factor in marble cutting process using ANFIS and ANN. Pamukkale University Journal of Engineering Sciences, 15(2), 221-226.
- Čdina, M. (2003). Detection of cavitation phenomenon in a centrifugal pump using audible sound. Mechanical systems and signal processing, 17(6), 1335-1347.
- Demirel, O., Kakilli, A., & Tektas, M. (2010). Electric energy load forecasting using ANFIS and ARMA methods.
- Guney, K., & Sarikaya, N. (2007). Adaptive neuro-fuzzy inference system for computing the resonant frequency of electrically thin and thick rectangular microstrip antennas. International Journal of Electronics, 94(9), 833-844. Gurbuzdal, F. (2009). Scale effects on the formation of vortices at intake structures. M. Sc. degree, scienc civil engineering, middle east technial University.
- Hanson, B. (2000). Irrigation Pumping Plants Retrieved from UC Irrigation and Drainage Specialist:
- Jang, J.-S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685.
- Jang, J. (1996). Input selection for ANFIS learning, fuzzy systems. Proceedings of IEEE 5th International Fuzzy Systems. v2, 1493-1499.
- Karadoğan, H., & Ürün, N. (1996). Pompa Çıkışında Basınç Dalgalanmaları. Paper presented at the 2. Pompa Kongresi, İstanbul.
- Kumaş, K. (2014). Binalarda ısıtma yükü ihtiyacının belirlenmesi için yeni bir yaklaşım. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü,
- Nagahara, T., Sato, T., & Okamura, T. (2001). 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.
- Nasiri, M., Mahjoob, M., & Vahid-Alizadeh, H. (2011). Vibration signature analysis for detecting cavitation in centrifugal pumps using neural networks. Paper presented at the 2011 IEEE International Conference on Mechatronics.
- Nurşen, E. C. (2011). Santrifüj Pompalarda Kavitasyon Problemi ve Maksimum Emme Yüksekliği (MEY) Hesabı Paper presented at the 7. Pompa ve Vana Kongresi İstanbul.
- Rafiee, J., Arvani, F., Harifi, A., & Sadeghi, M. (2007). Intelligent condition monitoring of a gearbox using artificial neural network. Mechanical systems and signal processing, 21(4), 1746-1754.
- Rajakarunakaran, S., Venkumar, P., Devaraj, D., & Rao, K. S. P. (2008). Artificial neural network approach for fault detection in rotary system. Applied Soft Computing, 8(1), 740-748.
- Sakthivel, N., Nair, B. B., Sugumaran, V., & Rai, R. S. (2011). Decision support system using artificial immune recognition system for fault classification of centrifugal pump. International Journal of Data Analysis Techniques and Strategies, 3(1), 66-84.
- Sakthivel, N., Sugumaran, V., & Babudevasenapati, S. (2010). Vibration based fault diagnosis of monoblock centrifugal pump using decision tree. Expert Systems with Applications, 37(6), 4040-4049.
- Sakthivel, N., Sugumaran, V., & Nair, B. B. (2010a). 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, 2(1), 38-61.
- Sakthivel, N., Sugumaran, V., & Nair, B. B. (2010b). Comparison of decision tree-fuzzy and rough set-fuzzy methods for fault categorization of mono-block centrifugal pump. Mechanical systems and signal processing, 24(6), 1887-1906.
- Wang, H., & Chen, P. (2007). Fault diagnosis of centrifugal pump using symptom parameters in frequency domain. Agricultural Engineering International: CIGR Journal. Wang, J., & Hu, H. (2006). Vibration-based fault diagnosis of pump using fuzzy technique. Measurement, 39(2), 176-185.
- Yang, B.-S., Oh, M.-S., & Tan, A. C. C. (2009). Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference. Expert Systems with Applications, 36(2), 1840-1849.
- Yüksel, E. and Eker, B., (2009a). Determination of Possible Wear on the Centrifugal Pump Wheel Used for Agricultural Irrigation Purposes. Journal of Tekirdag Agricultural Faculty, 6(2): 203-214.
- Yüksel, E. and Eker, B., (2009b). Determination of Wear That Can be Formed at the Stainless-Steel Wheels of the Centrifuge Pumps Used at Agricultural Irrigation. Journal of Tekirdag Agricultural Faculty, 2009; 6(3): 303-314.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Bölüm | Makaleler |
| Yazarlar | |
| Proje Numarası | 213O140 |
| Yayımlanma Tarihi | 20 Aralık 2021 |
| Gönderilme Tarihi | 13 Temmuz 2020 |
| Kabul Tarihi | 1 Eylül 2021 |
| Yayımlandığı Sayı | Yıl 2021 Cilt: 18 Sayı: 4 |
