Monitoring the Wear Trends in Wind Turbines by Tracking Fourier Vibration Spectra and Density Based Support Vector Machines

Claudiu Bisu, Adrian Olaru (), Serban Olaru, Adrian Alexei, Niculae Mihai and Haleema Ushaq
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Claudiu Bisu: National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania
Adrian Olaru: National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania
Serban Olaru: Military Equipment and Technology Research Agency, 077025 Clinceni, Romania
Adrian Alexei: Military Equipment and Technology Research Agency, 077025 Clinceni, Romania
Niculae Mihai: Concordia Technical University, Montreal, QC H4B 1R6, Canada
Haleema Ushaq: National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania

Mathematics, 2024, vol. 12, issue 9, 1-23

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 the development of condition-based predictive maintenance methodologies for wind turbines can enhance their efficiency and reliability. This paper presents a monitoring method that utilizes Density Based Support Vector Machines (DBSVM) and the evolutionary Fourier spectra of vibrations. This method allows for the 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 DBSVM to be timely determined from the Fourier spectrum through the magnitude–frequency and place of the failure occurring in the wind turbine drivetrains. The trend of the failure was constructed with the maximal values of the optimal frequency function for both yesthe cases of the upwind and downwind parts of the gearbox.

Keywords: wind turbine; monitoring; wear trend; Fourier vibration spectrum; support vector machine; base density of the collected data; machine learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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