## 1 Introduction  
Over the last years, customers who have wanted to monitor the real-time running condition of the machine's working process have studied and developed various monitoring methods. An important difference in these methodologies can be summarized as the different measuring system. Generally, the machine monitoring system can be classified as utilizing either contact or non-contact measurement. Contact approaches are mainly aimed at vibration and strain signals to collect dynamic information, however, carrying out of such methods bears the disadvantages of limited detecting positions, which are strict with respect to the measuring directions, and have difficult-to-maintain detectors in some severe situations [1]. Non-contact methods such as acoustic-based, image-based and laser vibrometry-based systems, however, can be used conveniently and do not affect the machine, thereby increasing efficiency and allowing online supervisory control [2; 3; 4]. The acoustic signal has attracted much attention in industrial monitoring due to its strong robustness to illumination intensity, air quality and barrier objects compared with the image [5]. In addition, it involves a lower cost and simpler structure than the laser vibrometry measuring system. Unfortunately, one of the most important unfavorable factors for the acoustic-based monitoring system is the substantial noise component in the initial sound signal [6]. Thus, the denoising operation is essential and the performance of noise elimination directly influences the effect of subsequent processing. Typical denoising methods can be divided into three types: time domain analysis, frequency domain analysis and joint time-frequency analysis [7]. Many researchers pointed out that methods considering both scales perform better than those focusing on only one. The denoising process can be translated into elimination of noise elements and keeping useful components as much as possible in the two domains. With respect to the development of signal denoising, the most influential time-frequency joint analysis methods were short-time Fourier transform (STFT)-based schemes [8], wavelet transform (WT)-based schemes [9] and empirical mode decomposition (EMD)-based schemes [10]. Due to the fixed window function during STFT, there exists an obvious contradiction between the time domain resolution and the frequency domain, which is inappropriate for signaling with complicated components [11]. On the basis of STFT, the concepts of multi-scale and multi-resolution were introduced into WT to overcome the problem. Then, the soft and hard threshold denoising methods were proposed by [PERSON] based on WT and became the fundamental theory of modern denoising approaches [12; 13]. However, the selection of wavelet basis and decomposition level depended on previous experience and trails, and strict mechanisms were scarce. Since it was proposed by [PERSON] et al. in 1998 [14], EMD has been widely applied for biomedical [15], fault diagnosis [16; 17], and audio recognition [18] applications, among others. EMD exhibits obvious advantages in terms of non-linear, non-stationary and intermittent signals compared to STFT and WT due to its adaptive basis, which usually offers a physically meaningful representation of the underlying processes. As a powerful adaptive decomposition tool, EMD decomposes the original signal into a series of intrinsic mode functions (IMFs) and is also well suited to estimating the noise or frequency in measurement domains. The EMD-based denoising system was widely used in Lidar detection [19], the inertial navigation system [20], and acoustic signal [21], among others. The first attempt at applying EMD as a noise elimination tool emerged from the need to determine whether a specific IMF primarily contains useful information or noise [22]. The EMD denoising scheme can be regarded as partially reconstructing the signal, retaining only the IMFs that include useful information and discarding those contain primarily noise.