A Digital Twin Development Framework for an Electrical Submersible Pump (ESP)
Bibliographic record
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. Received: 5 November 2023 | Revised: 5 February 2024 | Accepted: 8 March 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The information/data required for reproducing the results is already presented in the manuscript. Author Contribution Statement Mihiran Galagedarage Don: Conceptualization, Methodology, Software, Formal analysis, Data curation, Writing - original draft, Visualization, Project administration. Sampath Liyanarachchi: Investigation, Writing - review & editing. Thumeera R. Wanasinghe: Conceptualization, Methodology, Software, Validation, Resources, Writing - review & editing, Supervision, Project administration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".