Assessment of the Condition of Pump Units Based on Vibration Diagnostic Indicators supported by artificial intelligence (AI) and completed with Bibliometric Literature Review

Rustam Rakhimovich Ergashev, Gulrukhsor Surkhonidinovna Ergasheva, Fakhriddin Atabaevich Bekchanov, Dilsora Nuralikizi Kayumova, Feruza Rakhimovna Teshaboeva, Nargiza Gaibullayevna Dilova, Ravshanbek Rizomatovich Karimov

Abstract


This study assesses the condition of centrifugal pump units using vibration diagnostics supported by artificial intelligence and a bibliometric literature review. A refurbished pump operating in turbid water, simulating irrigation challenges, was tested. Vibration data were collected via accelerometers under different operating states. Diagnostic features were extracted using Fourier and wavelet analyses, then used to train a convolutional neural network (CNN). The CNN accurately identified faults such as unbalance, bearing wear, cavitation, and misalignment. Compared to traditional methods, the AI-based system improved fault detection speed and reduced false alarms. Results show that integrating vibration monitoring with intelligent classification enables early, reliable fault detection, extending pump service life and lowering maintenance costs. This method is especially relevant for predictive maintenance in developing regions with critical water infrastructure. The bibliometric analysis also highlights key trends in smart diagnostics, supporting future research on intelligent pump monitoring

Keywords


Artificial intelligence; Bibliometric review; Centrifugal pumps; Predictive maintenance; Vibration diagnostics

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References


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DOI: https://doi.org/10.17509/ajse.v5i3.88759

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