Machine Learning-Based Prediction of NPSH, Noise, and Vibration Levels in Radial Pumps Under Cavitation Conditions

Year 2024, , 533 - 546, 13.03.2024

Nuri Orhan , Mehmet Kurt , Hasan Kırılmaz , Murat Ertuğrul

https://doi.org/10.33462/jotaf.1324561

Abstract

Cavitation, a physical phenomenon that detrimentally affects pump performance and reduces pump life, can cause wear on pump elements. Various engineering methods have been developed to identify the initiation and full development of the cavitation process. One such method is the determination of the net positive suction head (NPSH) through a 3% decrease in total head (Hm) at a constant flow rate. In radial pumps, commonly used in agricultural irrigation and industry, cavitation conditions result in a sudden drop in the Hm-Q curve, making it challenging to detect the 3% Hm value drop. This study differs from others in the literature by modelling NPSH, noise, and vibration levels using three machine learning models, specifically artificial neural networks (ANN), support vector machines (SVM), and decision tree regression (DTR). The best-performing model predicts NPSH, noise, and vibration levels corresponding to a 3% decrease in Hm level. The present study determined the NPSH values of a horizontal shaft centrifugal pump at different flow rates and constant operating speed, and the vibration and noise levels were measured for these NPSH values. For each of the NPSH, noise, and vibration levels, ANN, SVM and DTR models were created. The performances of these models were evaluated using criteria such as root mean squared error (RMSE), Mean Absolute Error (MAE) and mean absolute percentage error (MAPE). In addition, Taylor and error box diagrams were created. The ANN model and DTR yielded high accuracy predictions for NPSH values (R2 = 0.86 and R2 = 0.8, respectively). The ANN model provided the best prediction performance for noise and vibration levels. By entering the level of 3% drop in the Hm value of the pump as external data input to the ANN model, NPSH3, noise, and vibration levels were determined. The ANN models can be effectively employed to determine NPSH3, noise, and vibration levels, particularly in radial flow pumps, where detecting 3% reductions in manometric height value is challenging.

Keywords

Centrifugal pumps, Net positive suction head (NPSH), Vibration, Noise, Machine learning

Radyal Pompalarda Kavitasyon Koşulları Altında ENPY, Gürültü ve Titreşim Düzeylerinin Makine Öğrenimine Dayalı Tahmini

Abstract

Kavitasyon, pompa performansını olumsuz etkileyen ve pompa ömrünü azaltan fiziksel bir olgudur ve pompa elemanlarında aşınmaya neden olabilir. Kavitasyon sürecinin başlangıcını ve tam gelişimini belirlemek için çeşitli mühendislik yöntemleri geliştirilmiştir. Bunlardan biri, sabit bir debi hızında toplam basınç düşüşü (%3 Hm) ile emmedeki net pozitif yük (ENPY) değerinin belirlenmesidir. Tarım sulaması ve endüstride yaygın olarak kullanılan radyal pompalarda, kavitasyon koşulları Hm-Q eğrisinde ani bir düşüşe yol açarak %3 Hm değer düşüşünü tespit etmeyi zorlaştırır. Bu çalışma, yapay sinir ağları (ANN), destek vektör makineleri (SVM) ve karar ağacı regresyonu (DTR) olmak üzere üç makine öğrenmesi modeli kullanarak ENPY, gürültü ve titreşim seviyelerini modellenmesiyle literatürdeki diğer çalışmalardan farklılık gösterir. En iyi performans gösteren model, %3 Hm düşüşüne karşılık gelen ENPY, gürültü ve titreşim seviyelerini tahmin eder. Bu çalışma, yatay şaftlı santrifüj pompada farklı debi hızlarında ENPY değerlerinin belirlendiği ve bu ENPY değerleri için titreşim ve gürültü seviyelerinin ölçüldüğü bir çalışmadır. ENPY, gürültü ve titreşim seviyeleri için ANN, SVM ve DTR modelleri oluşturulmuştur. Bu modellerin performansları kök ortalama kare hatası (KOKH), ortalama mutlak hata (OMH) ve ortalama mutlak yüzde hatası (OMYH) gibi kriterler kullanılarak değerlendirildi. Ayrıca Taylor ve hata kutu diyagramları oluşturulmuştur. ANN modeli ve DTR, ENPY değerleri için yüksek doğrulukta tahminler sağlamıştır (sırasıyla R2 = 0.86 ve R2 = 0.8). ANN modeli, gürültü ve titreşim seviyeleri için en iyi tahmin performansını sağlamıştır. Pompa Hm değerindeki %3 düşüş seviyesini ANN modeline harici veri girişi olarak kullanarak, ENPY3, gürültü ve titreşim seviyeleri belirlenmiştir. ANN modelleri, özellikle radyal akış pompalarında manometrik yükseklik değerlerinde %3'lük azalmaların tespit edilmesinin zor olduğu durumlarda, ENPY3, gürültü ve titreşim seviyelerini belirlemek için etkili bir şekilde kullanılabilir.

Keywords

Santrifüj pompalar, Emmedeki net pozitif yük (ENPY), Titreşim, Gürültü, Makine öğrenimi

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