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Full-Text Articles in Ocean Engineering
Artificial Intelligence In Prediction Of The Remaining Useful Life Of Wind Turbine Shaft Bearings, Jinsiang Shaw, B.J. Wu
Artificial Intelligence In Prediction Of The Remaining Useful Life Of Wind Turbine Shaft Bearings, Jinsiang Shaw, B.J. Wu
Journal of Marine Science and Technology
Long-term periodic rotation and unstable load changes in wind turbines can cause unexpected damage to high-speed shaft bearings (HSSBs). In this study, after preprocessing of the HSSB vibration signal, four different models for predicting bearing degradation in terms of remaining useful life (RUL) in days were investigated: support vector regression (SVR), convolutional neural networks (CNN), long short-term memory (LSTM), and CNN-LSTM. The experimental results revealed that the CNN achieved the best mean absolute error (MAE), at 0.44 days, based on frequency response plot using the fast Fourier transform (FFT), while that of the CNN-LSTM model predicted using the amplitude profile …
Prediction Of Remaining Useful Life Of Wind Turbine Shaft Bearings Using Machine Learning, Jinsiang Shaw, Bingjie Wu
Prediction Of Remaining Useful Life Of Wind Turbine Shaft Bearings Using Machine Learning, Jinsiang Shaw, Bingjie Wu
Journal of Marine Science and Technology
Wind turbines are a major trend in the current green energy market. Wind energy is abundant, and if utilized properly, can result in significant reductions in carbon emissions. Therefore, the development of wind power systems is urgently required. However, wind turbines are mainly built in unmanned areas. Regular inspections require substantial manpower and material resources, and doubts regarding the accuracy of the inspected data may occur. Therefore, it is necessary to establish an automatic diagnostic method for determining the remaining useful life (RUL) of a wind turbine to facilitate predictive maintenance. In this study, a multi-class support vector machine (SVM) …