## 1 Introduction  
Rolling bearing is widely used and plays an important role in rotating machinery. With the increasing speeds of modern rotating machinery, the dynamic loads on bearings have become increasingly complicated and adverse [1; 2]. As such, the abrasion of bearings has become an increasingly serious concern. These issues impede normal operation and may have negative safety consequences. Therefore, accurate monitoring and diagnosis of bearing working conditions is important for the normal operation of rotating machinery. However, when bearings damaged, the damage point repeatedly impacts the surface of other components in contact with it, resulting in erratic vibrations. Therefore, bearing vibration signals are often non-linear and non-stationary and contain a variety of frequency components, which distorts the signal. Bearing faults are difficult to correctly detect and effectively identify. Therefore, to improve bearing fault detection and diagnosis, it is essential to study representative feature extraction and intelligent fault pattern recognition. Signal processing and feature extraction are crucial in fault diagnosis. To effectively manage non-linear and non-stationary signals, time-frequency analysis methods are widely used. Comparedwith Wigner-Ville distribution (WVD) [3], wavelet transform has been fully applied in bearing fault detection [4; 5; 6] because of its non-crossover term and flexible choice in wavelet function. Different wavelet bases are suitable for analyzing different time-frequency characteristics. For inexperienced researchers, choosing the wavelet base poses a considerable challenge. Empirical mode decomposition (EMD) [7] decomposes the complex signal into finite stationary intrinsic mode functions (IMFs), which is an adaptive signal processing method independent of base function. Aimed to alias EMD modes [8], ensemble empirical mode decomposition (EEMD) [9], which has effectively improved problems of EMD mode aliasing, endpoint effects, and false components, was proposed. Due to multi-scale expression and a strong adaptive ability, EEMD is suitable for processing non-linear and non-stationary signals and has been successfully applied in health detection of mechanical equipment [10]. Therefore, in our research, EEMD was used to preprocess raw signals for multi-scale analysis. Rolling bearing vibration signals usually contain many interference frequency components, which distort the signal. Therefore, if the impact characteristics of a vibration signal can be effectively enhanced, fault features would be more accurately extracted and the diagnosis of the rolling bearing would improve. Permutation entropy (PE) [11] was proposed to detect the randomness and dynamic mutation of time series, having high computational efficiency, a stable computational value, strong anti-noise ability, and suitability for online monitoring. Therefore, PE has been widely applied in time series analysis [12; 13]. Based on the complexity of the components in a mechanical system, it is necessary to conduct multi-scale analysis and processing to ensure the integrity of the local and overall information of the vibration signal. Therefore, multiscale permutation entropy (MPE) [14], considering its classification accuracy for the health of rolling bearings, was proposed to measure the complexity and randomness of time series on different scales. Taking the extracted features as input, intelligent classification algorithms are used for fault pattern recognition. Artificial neural network (ANN) and support vector machine (SVM) are the two most commonly used machine learning methods for fault diagnosis. ANN has outstanding learning, associative storage, and high-speed optimization ability [15; 16]. [PERSON] et al. [17] used wavelet packet transform (WPT) to extract features of bearing vibration signal as input of ANN, which has achieved good fault classification accuracy.