Early Detection of Cavitation in Centrifugal Pumps Using Low-Cost Vibration and Sound Sensors

Marios Karagiovanidis, Xanthoula Eirini Pantazi (), Dimitrios Papamichail and Vassilios Fragos
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Marios Karagiovanidis: Laboratory of Agricultural Engineering, Faculty of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Xanthoula Eirini Pantazi: Laboratory of Agricultural Engineering, Faculty of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Dimitrios Papamichail: Laboratory of General & Agricultural Hydraulics & Land Reclamation, Faculty of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Vassilios Fragos: Laboratory of Agricultural Engineering, Faculty of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece

Agriculture, 2023, vol. 13, issue 8, 1-26

Abstract: The scope of this study is the evaluation of early detection methods for cavitation phenomena in centrifugal irrigation pumps by analyzing the produced vibration and sound signals from a low-cost sensor and data acquisition system and comparing several computational methods. Vibration data was acquired using the embedded accelerometer sensor of a smartphone device. Sound signals were obtained using the embedded microphone of the same commercial smartphone. The analysis was based on comparing the signals in different operating conditions with reference to the best efficiency operating point of the pump. In the case of vibrations, data was acquired for all three directional axes. The signals were processed by computational methods to extract the relative features in the frequency domain and use them to train an artificial neural network to be able to identify the different pump operating conditions while the cavitation phenomenon evolves. Three different classification algorithms were used to examine the most preferable approach for classifying data, namely the Classification Tree, the K-Nearest Neighbor, and the Support Vector Data algorithms. In addition, a convolutional neural network was utilized to examine the success rate of the classification when the datasets were formed as spectrograms instead. A detailed comparison of the classification algorithms and different axes was conducted. Comparing the results of the different methods for vibration and sound datasets, classification accuracy showed that in the case of vibration, the detection of cavitation in real conditions is possible, while it proves more challenging to identify cavitation conditions using sound data obtained with low-cost commercial sensors.

Keywords: centrifugal pump; cavitation; accelerometer; artificial neural network; predictive maintenance; signal analysis (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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