Data Driven Approach for Diagnostic and Prognostic of Vertical Motor-Driven Pump

This paper will present on data architecture that is used to collect heterogeneous data from vertical motor-driven pumps and how the collected data is used by the feature engineering module to extract salient features associated with different faults. Once fault signatures are developed, diagnostics models like eXtreme Gradient Boosting are used for automating the fault classification process. Given the diagnostic outcome, a prognostic model like autoregression integrated moving average is used to forecast the health condition of the motor-driven pump. Along with prediction horizons for 12, 24, and 48 hours, uncertainty bounds are also computed. This allows nuclear power plants to achieve condition-based maintenance and reduce unnecessary downtime, achieving significant cost-savings.

Keywords: Diagnosis, Prognosis, Autoregression integrated moving average, Uncertainty bounds, Classification, Condition-based maintenance.

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