Open Access. Powered by Scholars. Published by Universities.®

Engineering Commons

Open Access. Powered by Scholars. Published by Universities.®

Mechanical Engineering

PDF

Journal of Marine Science and Technology

SVM

Publication Year

Articles 1 - 2 of 2

Full-Text Articles in Engineering

Prediction Of Remaining Useful Life Of Wind Turbine Shaft Bearings Using Machine Learning, Jinsiang Shaw, Bingjie Wu Nov 2021

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) …


Application Of A Support Vector Machine For Liquefaction Assessment, Ching-Yinn Lee, Shuh-Gi Chern Jun 2013

Application Of A Support Vector Machine For Liquefaction Assessment, Ching-Yinn Lee, Shuh-Gi Chern

Journal of Marine Science and Technology

This study presents a support vector machine (SVM)-based approach for predicting earthquake liquefaction. The SVM model database includes five indexes: earthquake magnitude, total overburden pressure, effective overburden pressure, qc values from cone penetration tests (CPT), and peak ground acceleration. The proposed model was trained and tested on a dataset comprising 466 field liquefaction performance records and CPT measurements. A grid search method with k-fold cross-validation was also used to verify the feasibility. Compared with an artificial neural network (ANN)–based method, the SVM-based method has the advantage of increased accuracy and simpler operation. Experimental results show that the proposed SVM approach …