**Zhi Zheng 1, Zhijun Wang 1,\*, Yong Zhu 2,\*, Shengnan Tang <sup>2</sup> and Baozhong Wang <sup>1</sup>**


Received: 8 October 2019; Accepted: 31 October 2019; Published: 6 November 2019

**Abstract:** There are many interference components in Fourier amplitude spectrum of a contaminated fault signal, and thus the segment obtained based on the spectrum can lead to serious over-decomposition of empirical wavelet transform (EWT). Aiming to resolve the above problems, a novel method named improved empirical wavelet transform (IEWT) is proposed. Because the power spectrum is less sensitive to the contaminated interference and manifests the presence of fault feature information, IEWT replaces the Fourier amplitude spectrum of EWT with power spectrum in segment acquirement, and threshold processing is also introduced to eliminate the bad influence on the acquirement, and thus the best decomposition result of IEWT can be obtained based on feature energy ratio (*FER*). The loose slipper fault signal of hydraulic pump is tested and verified. The result demonstrates that the proposed method is superior and can extract the fault feature information accurately.

**Keywords:** hydraulic pump; fault signal; feature extraction; empirical wavelet decomposition; power spectrum density; feature energy ratio
