《机电工程》杂志社科技期刊核心期刊论文投稿机电一体化机械、仪表电工自动化、计算机浙江大学主办

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Fault identification of airborne fuel pump based on CEEMDAN-MFDE-HHO-SVM

Published:2023-11-01 author:LIU Junlong, YU Kaiyao, ZHANG Xiangchun. Browse: 1177 Check PDF documents

Fault identification of airborne fuel pump based on

CEEMDAN-MFDE-HHO-SVM

LIU Junlong1, YU Kaiyao2, ZHANG Xiangchun3

(1.School of Resources an Environment, Zunyi Normal College, Zunyi 563006, China; 2.College of Marine Equipment Engineering,

Zhejiang International Maritime College, Zhoushan 316012, China; 3.School of Biology and Agricultural Technology (College of Food

Science and Technology), Zunyi Normal University, Zunyi 563006, China)

Abstract: Aiming at the problem that effective components of vibration signal of airborne fuel pump were coupled to each other and fault feature extraction was difficult, which led to low fault identification accuracy, a fault identification method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), multiscale fluctuation dispersion entropy (MFDE), and Harris hawk algorithm (HHO) optimized support vector machine (SVM) was proposed (CEEMDAN-MFDE-HHO-SVM). Firstly, CEEMDAN method was used to decompose the airborne fuel pump vibration signal, generating a set of intrinsic mode functions (IMF) distributed from low frequency to high frequency, and IMF components containing more impact information were selected for signal reconstruction to obtain signals with lower noise content. Then, the MFDE method was used to calculate the entropy value of the low noise signal and a feature matrix that characterized the fault attributes of the sample was constructed. Finally, the HHO algorithm was used to optimize the key parameters of SVM to construct a multi-fault classifier based on HHO-SVM model, and fault identification of the airborne fuel pump was completed. A comparative analysis was conducted between the CEEMDAN-MFDE-HHO-SVM method and other combination methods based on the measured airborne fuel pump fault dataset. The results show that the classification accuracy of the fault identification model reaches 100%, which is superior to other comparison method in signal processing, entropy feature extraction and classifier. It not only has higher classification accuracy but also has better efficiency, which can be extended to other mechanical equipment fault identification in the future.

Key words: pump; fault identification accuracy; complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN); multi-scale fluctuation dispersion entropy(MFDE); Harris hawk optimization (HHO); support vector machine (SVM)

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