Smart Predictive Maintenance for Centrifugal Pumps: How IoT Sensors Reduce Downtime by Monitoring Vibration, Current, and Seal Integrity
DOI:
https://doi.org/10.25139/ijair.v7i1.10398Keywords:
Preventive Maintenance, Three-Phase Motor, Centrifugal Pump, Internet of Things (IOT), Research and Development MethodAbstract
This study presents the development of an IoT-based real-time monitoring system designed to optimize predictive maintenance protocols for three-phase industrial centrifugal pump motors. The investigation focuses on three critical failure parameters—mechanical seal integrity, motor current load, and pump vibration—which serve as primary indicators of incipient equipment failure. Employing a Research and Development (R&D) methodology, the system incorporates three specialized sensors: an ADXL345 triaxial vibration sensor exhibiting average 0,69% measurement error across X and Z axes), a high-sensitivity mechanical seal leakage detector (demonstrating 100% detection accuracy), and an SCT-013 non-invasive current transducer with average 6.0% measurement error. The IoT-enabled platform facilitates real-time data acquisition, visualization, and automated alarm generation based on thresholds. Experimental validation confirms the system's efficacy in early fault detection, with spectral vibration analysis proving particularly effective in diagnosing bearing wear and shaft misalignment. Current monitoring reliably identified overloading conditions, while the seal integrity system instantaneously detected fluid containment failures. NoTable limitations include the restricted scope of field testing under varied operational conditions and pump configurations. The study did not encompass complementary failure indicators such as thermal variations or high-frequency bearing vibrations. However, these constraints suggest that the implementation of the Internet of Things (IoT) could substantially reduce unplanned downtime in industrial applications. Future research directions include optimizing the cost-benefit ratio for small to medium-sized enterprises and integrating supplementary predictive parameters. This work is a significant step forward in developing Industry 4.0-compliant maintenance solutions.
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