TITLE:
Towards a Hybrid Digital Twin for Intelligent Monitoring and Predictive Maintenance of Hydraulic Turboalternator Group
AUTHORS:
Desire Tere Djacba, Colince Welba, Ruben Torres Martinez
KEYWORDS:
Hydraulic Turboalternator Group, Hybrid Digital Twin, Predictive Maintenance, LSTM, Hybrid Stochastic Automaton
JOURNAL NAME:
Journal of Computer and Communications,
Vol.14 No.7,
July
24,
2026
ABSTRACT: This study proposes a hybrid digital twin framework for intelligent monitoring and predictive maintenance of Hydraulic Turboalternator Groups (HTG), integrating high-fidelity physical modeling, a Hybrid Stochastic Automaton (HSA), and Long Short-Term Memory (LSTM) neural networks. The framework exploits the strengths of physics-based and data-driven approaches to enhance system reliability under varying operational conditions. The physical model accurately simulates the hydraulic and electromechanical dynamics, while the HSA represents discrete operational states and probabilistic transitions between normal, degraded, and fault conditions. The LSTM network, originally designed for dynamic control, is repurposed to analyze temporal sensor data, enabling early anomaly detection and identification of incipient degradation patterns. By fusing these components, the digital twin provides predictive maintenance insights, allowing interventions to be scheduled proactively, rather than reactively. Validation using real operational datasets demonstrates accurate forecasting of faults, classification of anomaly criticality, and optimization of maintenance intervals. Implementation of this predictive strategy reduces unplanned downtime by 27% and increases overall system availability by 12%, highlighting the operational benefits of integrating hybrid modeling with deep learning. This hybrid digital twin represents a next-generation methodology for asset health management in complex hydraulic energy systems. It bridges the gap between first-principles physics, stochastic modeling, and AI-based prognostics, offering a scalable, robust, and data-intelligent solution for proactive maintenance and operational resilience.