Version 1
: Received: 20 March 2024 / Approved: 20 March 2024 / Online: 20 March 2024 (10:20:01 CET)
Version 2
: Received: 9 April 2024 / Approved: 9 April 2024 / Online: 9 April 2024 (10:23:59 CEST)
Bisu, C.; Olaru, A.; Olaru, S.; Alexei, A.; Mihai, N.; Ushaq, H. Monitoring the Wear Trends in Wind Turbines by Tracking Fourier Vibration Spectra and Density Based Support Vector Machines. Mathematics2024, 12, 1307.
Bisu, C.; Olaru, A.; Olaru, S.; Alexei, A.; Mihai, N.; Ushaq, H. Monitoring the Wear Trends in Wind Turbines by Tracking Fourier Vibration Spectra and Density Based Support Vector Machines. Mathematics 2024, 12, 1307.
Bisu, C.; Olaru, A.; Olaru, S.; Alexei, A.; Mihai, N.; Ushaq, H. Monitoring the Wear Trends in Wind Turbines by Tracking Fourier Vibration Spectra and Density Based Support Vector Machines. Mathematics2024, 12, 1307.
Bisu, C.; Olaru, A.; Olaru, S.; Alexei, A.; Mihai, N.; Ushaq, H. Monitoring the Wear Trends in Wind Turbines by Tracking Fourier Vibration Spectra and Density Based Support Vector Machines. Mathematics 2024, 12, 1307.
Abstract
To make wind power more competitive, it is necessary to reduce turbine downtime and reduce costs associated with wind turbine Operation and Maintenance (O&M). Incorporating machine learning in developing condition-based predictive maintenance methodologies for wind turbines can enhance their efficiency and reliability. This paper presents a monitoring method that utilizes Base Density for the Support Vector Machine (BDSVM) and the evolutionary Fourier spectra of vibrations. This method allows smart monitoring of the function evolution of the turbine. A complex optimal function (FO) for 5-degree order has been developed that will be the boundary function of the BDSVM to timely determine the magnitude, frequency, and place of the failure occurring in wind turbine drive.
Keywords
wind turbine; monitoring; wear trend; Fourier vibration spectrum; support vector machine; base density of the collected data; machine learning.
Subject
Engineering, Control and Systems Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
