## 1 Introduction
[35] used the local minima as segmentation boundaries and then conducted the spectrum segmentation based on the scale space representation with a pre-determined scale parameter. Finally, satisfying modes can be obtained by merging similar mono-components together. However, these modifications ignore the spectrum shape of rolling element bearing vibration signal in noise conditions and fail to get a reasonable segmentation result. In order to avoid the inaccurate identification of the internal modes caused by the heavy background noise, pre-treatment or post-treatment are combined with the EWT to enhance the feature extraction performance. [PERSON] et al. [36] used the wavelet spatial neighboring coefficient de-noising with data-driven threshold to increase the signal to noise ratio (SNR) before EWT. The effectiveness of the proposed method has been validated by analyzing two experimental cases. By developing a feasible and efficient spectrum segmentation method, [PERSON] et al. [37] also proposed an enhanced EWT and validated the effectiveness of this method by using the bearing and gearbox signals. In 2016, [PERSON] et al. [38] utilized EWT to decompose the acoustic emission (AE) signals to extract the bearing fault feature by calculating the correlated kurtosis (CK) of the mono-components. However, a suitable time interval in the calculation of CK is difficult to pre-define and the performance of the proposed method is not good enough for engineering applications. Therefore, it can be included that although literatures report variant EWT technologies for rolling element bearings, the faults diagnosis in heavy noise condition is still a difficult task. In order to diagnose faults of the rolling element bearings in strong noise condition, the EWT is improved by calculating the envelope of amplitude spectrum, and a new fault feature extraction method based on the combination of the Maximum Correlated Kurtosis Deconvolution (MCKD) and improved EWT is proposed in this paper. Already proven as an efficient tool for enhancing the periodic impulses in fault diagnosis of rolling element bearings and gearboxes [39; 50; 51], the MCKD is firstly used to de-noise the collected vibration signal. Then, the vibration signal is decomposed into a set of modes by the improved EWT, which performs segmentation based on the local maxima or minima of the amplitude spectrum envelope. Finally, by calculating the squared envelope spectrum, the fault feature can be extracted from the selected EWT mode. The remainder of this paper is organized as follows. The basic processes of MCKD and EWT are introduced in Section 2. Then, the EWT is improved and the fault feature extraction method is proposed by the combination of the MCKD and improved EWT in Section 3. Section 4 presents the preliminary validation of MCKD-EWT using simulations. Additionally, comparisons with the EMD, EWT, and improved EWT are also performed in this section. In Section 5, the real vibration signals from the bearing test rig are utilized to further validate the effectiveness of the proposed method by comparing the proposed method with FK (Fast Kurtogram) and OWPT (Optimal Wavelet Packet Transform) methods. Conclusions are summarized in Section 6.