A Digital Twin Development Framework for an Electrical Submersible Pump (ESP)

Abstract

Premature failure of a subsystem can be critical for an industrial cyber-physical system (CPS). A digital twin (DT)-assisted predictive maintenance procedure can reduce the risk of costly unplanned maintenance. This study presents a generalized DT development framework for an electrical submersible pump (ESP) that can assist in predictive maintenance. The framework is applied on a single-phase ESP as a proof of concept. The maximum winding temperature of the selected ESP is simulated using a multiphysics simulation tool with transient electromagnetic and transient heat transfer solvers. The simulation parameters were refined using data captured through an ESP free-run experiment. Simulating the total energy loss in the ESP stator and rotor and the transfer of heat from the outer fluid domain facilitates a relationship between the measurable external temperature and the maximum temperature in the stator winding. Following a design of experiment approach, a series of simulations were run to establish a statistical model for the winding temperature in terms of the fluid temperature, the time duration a particular temperature was persistent, and the initial maximum stator winding temperature. As the instantaneous maximum stator winding temperature is related to the remaining useful lifetime, it was shown using a case study that the proposed framework can prognosticate the ESP failure, assisting effective decision-making for predictive maintenance of a CPS.

Keywords

cyber-physical systems / digital twin / electrical submersible pump / predictive maintenance

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