Comparative Study of Cavitation Problem Detection in Pumping System Using SVM and K-Nearest Neighbour Method

Dutta, Nabanita; Subramaniam, Umashankar; Sanjeevikumar, P.; Bharadwaj, Sai Charan; Leonowicz, Zbigniew; Holm‐Nielsen, Jens Bo

doi:10.1109/eeeic/icpseurope49358.2020.9160689

Cited by 4 publications

(5 citation statements)

References 21 publications

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“…k NN and SVM have also been used in cavitation models ( Fadaei Kermani et al, 2018 ; Dutta et al, 2020 ). For instance, a k NN model was able to accurately predict the severity of cavitation damage on a dam spillway during periods of flooding ( Fadaei Kermani et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

“…For instance, a k NN model was able to accurately predict the severity of cavitation damage on a dam spillway during periods of flooding ( Fadaei Kermani et al, 2018 ). Additionally, a comparative study of k NN and SVM has also been performed by Dutta et al (2020) to detect cavitation in a pumping system. The results showed that k NN is preferable when there are more training data than features (i.e., variables), while the SVM is better at classifying larger amounts of labeled data.…”

Section: Introductionmentioning

confidence: 99%

“…The results showed that k NN is preferable when there are more training data than features (i.e., variables), while the SVM is better at classifying larger amounts of labeled data. The labeled data in the study by Dutta et al (2020) were variables with assigned values, where these values described the centrifugal pumping system (e.g., cavitation status and rotational speed).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting shock-induced cavitation using machine learning: implications for blast-injury models

Marsh,

Zinnel,

Bentil

2024

Front. Bioeng. Biotechnol.

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While cavitation has been suspected as a mechanism of blast-induced traumatic brain injury (bTBI) for a number of years, this phenomenon remains difficult to study due to the current inability to measure cavitation in vivo. Therefore, numerical simulations are often implemented to study cavitation in the brain and surrounding fluids after blast exposure. However, these simulations need to be validated with the results from cavitation experiments. Machine learning algorithms have not generally been applied to study blast injury or biological cavitation models. However, such algorithms have concrete measures for optimization using fewer parameters than those of finite element or fluid dynamics models. Thus, machine learning algorithms are a viable option for predicting cavitation behavior from experiments and numerical simulations. This paper compares the ability of two machine learning algorithms, k-nearest neighbor (kNN) and support vector machine (SVM), to predict shock-induced cavitation behavior. The machine learning models were trained and validated with experimental data from a three-dimensional shock tube model, and it has been shown that the algorithms could predict the number of cavitation bubbles produced at a given temperature with good accuracy. This study demonstrates the potential utility of machine learning in studying shock-induced cavitation for applications in blast injury research.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting shock-induced cavitation using machine learning: implications for blast-injury models

Marsh,

Zinnel,

Bentil

2024

Front. Bioeng. Biotechnol.

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show abstract

“…Vibration analysis has been also applied to extract cavitation-related features, which can be used in classifiers for fault diagnosis. For that purpose, different types of classifiers and optimizers have been used in order to improve the calculation speed and diagnosis accuracy [7][8][9][10][11]. As the signal-based methods [12][13][14][15] for machinery diagnosis, as well as the model-based methods [16][17][18][19], have been applied, the research on both newer vibration analysis techniques and more advanced machine learning tools for accurate cavitation analysis is still continued.…”

Section: Introductionmentioning

confidence: 99%

Cavitation Analysis in Centrifugal Pumps Based on Vibration Bispectrum and Transfer Learning

Hajnayeb

2021

Shock and Vibration

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Detection of cavitation in centrifugal pumps is critical in their condition monitoring. In order to detect cavitation more accurately and confidently, more advanced signal processing techniques are needed. For the classification of a pump conditions based on the outputs of these techniques, advanced machine learning techniques are needed. In this research, an automatic system for cavitation detection is proposed based on machine learning. Bispectral analysis is used for analyzing the vibration signals. The resulting bispectrum images are given to convolutional neural networks (CNNs) as inputs. The CNNs are a pretrained AlexNet and a pretrained GoogleNet, which are used in this application through transfer learning. On the contrary, a laboratory test setup is used for generating controlled cavitation in a centrifugal pump. The suggested algorithm is implemented on the vibration dataset acquired from the laboratory pump test setup. The results show that the cavitation state of the pump can be detected accurately using this system without any need to image processing or feature extraction.

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“…The concern of this work is to analyze the acoustic emissions produced by the MBs when exposed to an acoustic field and explore AI for cavitation detection. The investigation to identify the phenomenon during the bubble activity is not recent in hydraulic systems, where the solutions based on Machine Learning (ML) are numerous (Dutta, Vishnu, et al 2018;B.-S. Yang et al 2005;Dutta, Subramaniam, et al 2020). For our scenario, we want to detect cavitation in a healthcare application.…”

Section: John Mccarthy Recognized As the Father Of Artificial Intelli...mentioning

confidence: 99%

Detecção e localização de cavitação via inteligência artificial.

Guenkawa

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Comparative Study of Cavitation Problem Detection in Pumping System Using SVM and K-Nearest Neighbour Method

Cited by 4 publications

References 21 publications

Predicting shock-induced cavitation using machine learning: implications for blast-injury models

Predicting shock-induced cavitation using machine learning: implications for blast-injury models

Cavitation Analysis in Centrifugal Pumps Based on Vibration Bispectrum and Transfer Learning

Detecção e localização de cavitação via inteligência artificial.

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