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Assessment of the Condition of Pump Units Based on Vibration Diagnostic Indicators to Support Sustainable Development Goals (SDGs) in Sustainable Water Infrastructure
Abstract
This study evaluates centrifugal pump health using vibration indicators and artificial intelligence for predictive maintenance in irrigation. A refurbished pump was equipped with accelerometers to capture signals under normal, underload, and overload conditions. Fast Fourier transform and wavelet packet analysis extracted features, which were classified by a convolutional neural network. The model distinguished unbalance, bearing wear, cavitation, misalignment, and normal operation with ~94% accuracy, maintaining robustness in turbid water. Integrating physics-based processing with AI proved effective: spectral and time-frequency features revealed fault mechanisms, while neural networks captured nonlinear patterns under noise. Compared to threshold monitoring, this method reduced false alarms by over one-third and halved fault detection time, enhancing reliability, reducing maintenance, and supporting the SDGs.
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DOI: https://doi.org/10.17509/ajse.v6i2.90208
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