Universiti Teknologi Malaysia Institutional Repository

Intelligent wind turbine gearbox diagnosis using VMDEA and ELM

Isham, Muhammad Firdaus and Leong, Muhammad Salman and Lim, Meng Hee and Ahmad, Zair Asrar (2019) Intelligent wind turbine gearbox diagnosis using VMDEA and ELM. Wind Energy, 22 (6). pp. 813-833. ISSN 1095-4244

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Official URL: http://dx.doi.org/10.1002/we.2323


Wind turbine gearbox diagnosis is a vital tool for maintaining wind turbine operation and safety. The gearbox vibration signal is invariably complex and variable, and useful information and features are difficulty of extraction. Recently, a new and adaptive signal decomposition method, known as variational mode decomposition (VMD), has been proposed, which helps to improve the efficiency and effectiveness of extracting features from gearbox vibration signals. However, the performance of the VMD method mainly depends on its input parameters, especially the mode number and balancing parameter (also called the quadratic penalty term). Hence, this paper proposes a selection method for an optimized VMD parameter using differential evolution algorithm (DEA), also called VMDEA. Firstly, the VMDEA is used to select optimized VMD input parameters for each of the vibration signals. Following this, VMD decomposes each vibration signal into sets of subsignals using the selected optimized parameter. Multidomain features are extracted from VMD reconstructed signals and are passed on to the extreme learning machine (ELM) for fault classification. This study can thus provide a good solution for determining an optimized VMD parameter for decomposing vibration signals and can also provide a more efficient and effective diagnostic approach to wind turbine gearbox maintenance.

Item Type:Article
Uncontrolled Keywords:fault diagnosis, gearbox
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:Electrical Engineering
ID Code:87388
Deposited By: Widya Wahid
Deposited On:08 Nov 2020 11:55
Last Modified:08 Nov 2020 11:55

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