Digital Twin-Enabled Predictive Maintenance Framework for Intelligent Fault Detection and Remaining Useful Life Prediction in Electrical Systems

Abstract

Failures in motors, transformers, switchgear, and converters can propagate from degradation to production interruption, safety exposure, revenue loss, and reputational damage. Conventional monitoring detects abnormalities but often lacks a synchronized asset model, quantified uncertainty, and a defensible link between technical evidence and managerial action. This paper develops a standards-informed digital twin-enabled prognostics and health management framework that integrates intelligent fault detection and probabilistic remaining useful life (RUL) prediction with lifecycle economics and strategic asset management. The framework couples an updated state-space twin with physics residuals, temporal learning, Bayesian estimation, explainability, risk-based maintenance optimization, and a managerial value layer. It specifies sensing, data quality, semantic context, hybrid model orchestration, uncertainty, cybersecurity, human authorization, asset criticality, spare-parts coordination, investment appraisal, and value-realization governance. Validation combines asset-disjoint chronological testing, fault-severity and regime stratification, calibration, warning lead time, decision utility, lifecycle cost, and organizational adoption indicators. Ten tables map failure modes, signals, twin layers, models, validation gates, economic metrics, stakeholders, and commercial options; three figures present the architecture, analytics pipeline, and deployment loop. Because no empirical dataset is supplied, no measured accuracy, cost saving, or return on investment is claimed. The contribution is a testable socio-technical framework for electrical assets that converts condition evidence into auditable engineering, financial, and managerial decisions without confusing a static model with an operational twin.