Real-Time Bearing Fault Detection in Wind Turbines: A Temperature-Based Severity Index Approach Using 8-Bit Thermal Imaging
Received: 21 February 2026 | Revised: 4 April 2026 and 23 April 2026 | Accepted: 30 April 2026 | Online: 30 May 2026
Corresponding author: Ala Eldin Abdallah Awouda
Abstract
Bearings in wind turbine drivetrains are susceptible to failure under variable operational loads, leading to costly downtime and maintenance. In this paper, a thermal monitoring-based fault diagnosis framework for wind turbine bearings using 8-bit infrared thermography is presented. The proposed methodology consists of three stages: (1) Acquisition of thermal images of bearing assemblies, (2) conversion of radiance data to temperature values using the Stefan-Boltzmann law, and (3) computation of a Severity Index (SI) based on the temperature differential (ΔT) between real-time measurements and a baseline profile. The proposed framework was implemented and validated through a MATLAB/Simulink simulation of a 1.5 MW horizontal-axis wind turbine, incorporating thermal dynamics of the main shaft bearing under varying wind speed conditions (4–25 m/s). The results show that the SI enables clear differentiation between normal and abnormal thermal states, with fault indications triggered at ΔT ≥ 8.5°C above the baseline. Compared to conventional fixed-threshold temperature monitoring, the proposed SI-based method enables earlier fault detection by identifying gradual thermal drift. The findings demonstrate that 8-bit thermal imaging, combined with a quantitative severity index, offers a practical and reproducible approach for predictive maintenance in wind turbine bearing systems.
Keywords:
bearing fault detection, wind turbine, thermal imaging, temperature-based severity indexReferences
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