SPECTRAL ANALYSIS METHOD FOR TECHNICAL CONDITION ASSESSMENT OF ELECTRIC DRIVES IN PUMP UNITS
DOI:
https://doi.org/10.37547/Keywords:
spectral analysis, pump units, electric drives, technical condition assessment, vibration diagnostics, predictive maintenance, fault detection, FFT analysis, condition monitoring, irrigation systems.Abstract
It is 2 a.m. at the Fergana Valley irrigation station. The night-shift engineer hears it first—a low hum, barely audible over the usual mechanical noise, rising from Pump Unit No. 3. He walks closer. The sound sharpens. Something is wrong, but what? The pressure gauge reads normal. The temperature is within limits. The motor current is stable. By every conventional indicator, the pump is operating perfectly. Yet that hum—the engineer knows from 20 years of experience—means failure is close. But how close? Hours? Days? And which component will fail first: the bearing, the rotor, the coupling? Without answers, he faces an impossible choice: shut down the pump now for unscheduled inspection (risking agricultural water shortages across 12,000 hectares), or wait and hope the failure happens during a planned maintenance window. This is the daily reality of pump unit operation in Uzbekistan, where more than 8,500 large-capacity pump stations deliver irrigation water to 4.3 million hectares of farmland. This article presents a systematic spectral analysis methodology that translates that ‘hum’ into precise, quantitative diagnostic information, enabling condition-based maintenance decisions that reduce unplanned downtime by 60–75%, extend equipment service life by 30–40%, and prevent catastrophic failures that can cost USD 50,000–150,000 in emergency repairs and lost agricultural production. The method combines accelerometer-based vibration measurement, Fast Fourier Transform (FFT) spectral decomposition, and machine learning classification to detect incipient faults in electric motor bearings, rotor misalignment, stator winding deterioration, and mechanical coupling wear at stages where intervention costs are minimal.
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