Real-time multi-fault diagnosis in wind turbines using a physics-guided hybrid RBF-ANN framework 核心 · 已核验

doi:10.6084/m9.figshare.32895238.v1

In this study, a real-time diagnostic system is proposed for fault detection in wind turbine power systems using a hybrid Radial Basis Function–Artificial Neural Network (RBF-ANN) framework. The proposed system analyzes environmental, mechanical, and electrical parameters, including wind speed, temperature, rotor speed, gearbox vibration, torque, voltage, and current, to identify multiple operating conditions. A dataset of more than 22,000 labelled samples was developed using real-time turbine simulator data and MATLAB/Simulink–FAST-based fault simulations. Six diagnostic classes were considered: healthy condition, generator fault, gearbox fault, rotor imbalance, electrical disturbance, and compound fault. The proposed RBF-ANN achieved an overall classification accuracy of 94.8% and a weighted F1-score of 0.942, outperforming SVM, k-NN, and MLP models, which achieved accuracies of 89.1%, 86.3%, and 91.5%, respectively. The model also demonstrated strong class-wise performance, with diagnostic accuracy ranging from 90.8% for compound faults to 98.5% for healthy operating conditions. Real-time implementation on a Raspberry Pi 4 produced an average inference latency of 192 ms per sample, with approximately 62% CPU utilization and 280 MB RAM usage, confirming its suitability for lightweight edge deployment. The false-positive rate remained below 4% across all fault categories, indicating reliable fault discrimination under varying operating conditions. These results demonstrate that the proposed RBF-ANN framework provides an accurate, interpretable, and computationally efficient solution for real-time wind turbine fault diagnosis and predictive maintenance. Developed a hybrid RBF-ANN framework for real-time fault detection in wind turbine power systems.Incorporated environmental and electrical parameters (wind speed, rotor speed, voltage, current) for multi-fault classification.Achieved high resilience to noise and fluctuations compared to traditional threshold/rule-based models.Successfully detected gear faults, generator malfunctions, and aerodynamic inefficiencies with improved precision.Demonstrated a 22% increase in detection accuracy over SVM and MLP approaches under varying loads and weather conditions. Developed a hybrid RBF-ANN framework for real-time fault detection in wind turbine power systems. Incorporated environmental and electrical parameters (wind speed, rotor speed, voltage, current) for multi-fault classification. Achieved high resilience to noise and fluctuations compared to traditional threshold/rule-based models. Successfully detected gear faults, generator malfunctions, and aerodynamic inefficiencies with improved precision. Demonstrated a 22% increase in detection accuracy over SVM and MLP approaches under varying loads and weather conditions.

落地页

https://tandf.figshare.com/articles/dataset/Real-time_multi-fault_diagnosis_in_wind_turbines_using_a_physics-guided_hybrid_RBF-ANN_framework/32895238

许可证

CC-BY-4.0 (判读置信:inferred)

国内可访问性

国内直连:可达 (2026-07-11 检测) 代理通道:可达 (2026-07-11 检测)
检测口径:lychee 双通道单轮探测;「直连超时」表示检测窗口内未完成,系慢或不稳定证据,不构成封锁证据。

设备类型

wind_turbine gearbox

PHM 任务

fault_detection fault_diagnosis

故障工况

运行工况