Fault diagnosis of flywheel bearing based on parameter optimization variational mode decomposition energy entropy and deep learning

Deqiang He, Chenyu Liu, Zhenzhen Jin, Rui Ma, Yanjun Chen and Sheng Shan

Energy, 2022, vol. 239, issue PB

Abstract: Flywheel energy storage system is widely used in train braking energy recovery, and has achieved excellent energy-saving effect. As a key component of the flywheel energy storage system, the health of the bearing is greatly significant to realize the effective recovery of train braking energy. The vibration signal of the bearing presents complex nonlinear and non-stationary characteristics, which makes it difficult to diagnose the fault of the bearing. To solve this problem, a fault diagnosis method for bearing of flywheel energy storage system based on parameter optimization Variational Mode Decomposition (VMD) energy entropy is proposed. Firstly, the improved Sparrow Search Algorithm is used to optimize VMD parameters with the dispersion entropy as the fitness value. Then, the original signal is decomposed into a series of intrinsic mode components by using the optimized VMD algorithm, and the energy entropy of each component is calculated to construct the feature vector. Finally, an Inverted Residual Convolutional Neural Network (IRCNN) is used as feature vector input model for fault diagnosis. The experimental results show that the proposed method can effectively extract the bearing fault characteristics and realize accurate fault diagnosis, and the recognition rate reaches 97.5%, which is better than the comparison method.

Keywords: Energy recovery; Flywheel energy storage system; Fault diagnosis; Inverted residual neural network (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:239:y:2022:i:pb:s0360544221023562

DOI: 10.1016/j.energy.2021.122108

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