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Entropy 2019, 21(4), 400; https://doi.org/10.3390/e21040400

Research on Fault Extraction Method of Variational Mode Decomposition Based on Immunized Fruit Fly Optimization Algorithm

School of Mechanical Engineering, North University of China, Taiyuan 030051, China
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Received: 22 February 2019 / Revised: 29 March 2019 / Accepted: 3 April 2019 / Published: 15 April 2019
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Abstract

In recent years, a new method of fault diagnosis, named variational mode decomposition (VMD), has been widely used in industrial production, but the decomposition accuracy of VMD is determined by two parameters, which are respectively the decomposition layer number k and the penalty factor α, if the parameters are not properly selected, there will be over-decomposition or under-decomposition. In order to find an approach to determine the parameters adaptively, a method to optimize VMD by using the immune fruit fly optimization algorithm (IFOA) is proposed in this paper. In this method, permutation entropy is used as the fitness function, firstly, the immune fruit fly optimization algorithm is used to search the combined parameters of k and α in VMD, searching for the best combination parameters of k and α by iteration, and then uses the combined parameters to perform VMD, finally, the center frequency is determined through frequency spectrum analysis. The method mentioned is applied to the fault extraction of a simulated signal and a measured signal of a wind turbine gearbox, and the fault frequency is successfully extracted. Using ensemble empirical mode decomposition (EEMD) and singular spectrum decomposition (SSD) to compare with the proposed method, which validated feasibility of the proposed method. View Full-Text
Keywords: VMD; IFOA; permutation entropy; fault diagnosis VMD; IFOA; permutation entropy; fault diagnosis
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Zhou, J.; Guo, X.; Wang, Z.; Du, W.; Wang, J.; Han, X.; Wang, J.; He, G.; He, H.; Xue, H.; Kou, Y. Research on Fault Extraction Method of Variational Mode Decomposition Based on Immunized Fruit Fly Optimization Algorithm. Entropy 2019, 21, 400.

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