Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

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Remaining useful life prediction of rotating machinery using thermal and fatigue analysis in a digital twin framework

Author(s) Dr. Jannavarapu Praveena, Ms. Sekharamahanthi Nitya, Ms. Maddukuri Nandini Satya
Country India
Abstract Rotating machinery plays an important role in many industrial systems such as pumps, compressors, turbines, and electric motors. Continuous operation of these machines under mechanical and thermal loads can gradually reduce the life of critical components like shafts and bearings. Predicting the remaining useful life (RUL) of such components is essential to avoid unexpected failures and reduce maintenance costs.
In this study, a digital twin-based framework is developed to monitor the thermal condition and fatigue life of a rotating shaft-bearing system. A shaft supported by a deep groove ball bearing is modelled using SolidWorks and analysed using ANSYS simulation software. Steady-state thermal analysis is performed to evaluate the temperature distribution in the system, while fatigue analysis is carried out to estimate the fatigue damage and life of the component under cyclic loading conditions.
The simulation results obtained from ANSYS are exported and processed using Python to analyse temperature distribution, fatigue life, and damage parameters. Based on these parameters, the digital twin model can detect abnormal thermal behaviour such as overheating and estimate the remaining useful life of the rotating component. The proposed approach demonstrates how simulation tools and data analysis techniques can be combined to support predictive maintenance and improve reliability of rotating machinery.
Keywords Remaining Useful Life, Thermal Analysis, Fatigue Behaviour, Digital Twin Model, Life Prediction, Rotating Components
Field Engineering
Published In Volume 7, Issue 3, May-June 2026
Published On 2026-05-09
DOI https://doi.org/10.63363/aijfr.2026.v07i03.5554
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E-ISSN 3048-7641
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