Hydraulic Pump Fault Diagnosis Based on Empirical Wavelet Transform and Extreme Learning Machine

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Published Jul 6, 2018
Yu Ding Chen Lu Laifa Tao Yujie Cheng Xingliu Zhang Dengwei Song

Abstract

In order to recognize the conditions of a hydraulic pump quickly and effectively, a fault diagnosis method based on empirical wavelet transform (EWT) and extreme learning machine (ELM) is proposed in this paper. EWT, a new self-adaptive signal decomposition method, is used to adaptively decompose the original fault signal from low frequency to high frequency according to frequency characteristics. After extracting the characteristics of the decomposed signal, ELM is applied to achieve the fault classification of hydraulic pump with its advantages of high learning speed and generalization performance. Experiments show that above method captures fault information of hydraulic pump well and performs feasible and effective.

How to Cite

Ding, Y., Lu, C., Tao, L., Cheng, Y., Zhang, X., & Song, D. (2018). Hydraulic Pump Fault Diagnosis Based on Empirical Wavelet Transform and Extreme Learning Machine. PHM Society European Conference, 4(1). https://doi.org/10.36001/phme.2018.v4i1.563
Abstract 341 | PDF Downloads 297

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Keywords

Hydraulic Pump, Fault Diagnosis, Empirical Wavelet Transform, Extreme Learning Machine

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