Maintenance of pumps is a routine activity carried out in all flow stations in the oil and gas industry, hence, there is a growing concern to optimize its operation to reduce downtime and maximize productivity. To this end, this study aimed at evaluating the effectiveness of parameter indices as indicators in Condition Based Monitoring (CBM) of centrifugal pump bearing. Leading/output indicators that interface with the maintenance schedule and procedure were evaluated. These indicators establish the current state of the plant and identify what parameters/activity is responsible for a noted trend. The relationship between the pump maintenance procedure and the state of the centrifugal pump was established using vibration analysis. This was achieved by using the Fast Fourier Transform (FFT) technique and validated using the Genetic Algorithm (GA) method. The result of the study shows that at the current operating condition of the flow station, failure in the pump is expected after 9480 hours of operation, and this failure is due to subsurface fatigue, and coupling damage. A model and chart were developed which is instructive in scheduling maintenance operations to avoid an unnecessary halt in the production line due to the failure of a component. The study concludes that the use of parameter indicators is a necessary guide in positioning a production line/manufacturing plant for effective condition-based monitoring.
Condition-based, Maintenance, Pumps, Genetic algorithm, Parameter Indicators
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