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Open AccessArticle

A Hydraulic Pump Fault Diagnosis Method Based on the Modified Ensemble Empirical Mode Decomposition and Wavelet Kernel Extreme Learning Machine Methods

by 1,2, 1,2,*, 1,2, 1,2 and 3
1
Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao 066004, China
2
Key Laboratory of Advanced Forging & Stamping Technology and Science, Yanshan University, Ministry of Education of China, Qinhuangdao 066004, China
3
School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
*
Author to whom correspondence should be addressed.
Academic Editor: Chris Karayannis
Sensors 2021, 21(8), 2599; https://doi.org/10.3390/s21082599
Received: 11 March 2021 / Revised: 31 March 2021 / Accepted: 5 April 2021 / Published: 7 April 2021
(This article belongs to the Section Fault Diagnosis & Sensors)
To address the problem that the faults in axial piston pumps are complex and difficult to effectively diagnose, an integrated hydraulic pump fault diagnosis method based on the modified ensemble empirical mode decomposition (MEEMD), autoregressive (AR) spectrum energy, and wavelet kernel extreme learning machine (WKELM) methods is presented in this paper. First, the non-linear and non-stationary hydraulic pump vibration signals are decomposed into several intrinsic mode function (IMF) components by the MEEMD method. Next, AR spectrum analysis is performed for each IMF component, in order to extract the AR spectrum energy of each component as fault characteristics. Then, a hydraulic pump fault diagnosis model based on WKELM is built, in order to extract the features and diagnose faults of hydraulic pump vibration signals, for which the recognition accuracy reached 100%. Finally, the fault diagnosis effect of the hydraulic pump fault diagnosis method proposed in this paper is compared with BP neural network, support vector machine (SVM), and extreme learning machine (ELM) methods. The hydraulic pump fault diagnosis method presented in this paper can diagnose faults of single slipper wear, single slipper loosing and center spring wear type with 100% accuracy, and the fault diagnosis time is only 0.002 s. The results demonstrate that the integrated hydraulic pump fault diagnosis method based on MEEMD, AR spectrum, and WKELM methods has higher fault recognition accuracy and faster speed than existing alternatives. View Full-Text
Keywords: hydraulic pump; fault diagnosis; modified ensemble empirical mode decomposition (MEEMD); wavelet kernel extreme learning machine (WKELM) hydraulic pump; fault diagnosis; modified ensemble empirical mode decomposition (MEEMD); wavelet kernel extreme learning machine (WKELM)
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MDPI and ACS Style

Li, Z.; Jiang, W.; Zhang, S.; Sun, Y.; Zhang, S. A Hydraulic Pump Fault Diagnosis Method Based on the Modified Ensemble Empirical Mode Decomposition and Wavelet Kernel Extreme Learning Machine Methods. Sensors 2021, 21, 2599. https://doi.org/10.3390/s21082599

AMA Style

Li Z, Jiang W, Zhang S, Sun Y, Zhang S. A Hydraulic Pump Fault Diagnosis Method Based on the Modified Ensemble Empirical Mode Decomposition and Wavelet Kernel Extreme Learning Machine Methods. Sensors. 2021; 21(8):2599. https://doi.org/10.3390/s21082599

Chicago/Turabian Style

Li, Zhenbao; Jiang, Wanlu; Zhang, Sheng; Sun, Yu; Zhang, Shuqing. 2021. "A Hydraulic Pump Fault Diagnosis Method Based on the Modified Ensemble Empirical Mode Decomposition and Wavelet Kernel Extreme Learning Machine Methods" Sensors 21, no. 8: 2599. https://doi.org/10.3390/s21082599

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