## 1 Introduction
PerEn is much simpler to calculate and considerably faster than SampEn; however, it considers only the order relations of the signal amplitude values, so some information regarding the amplitude differences may be lost. To tackle these problems, [PERSON] and [PERSON] [22; 23] recently introduced one new nonlinear parameter, termed dispersion entropy (DisEn), to quantify the complexity of a time series. DisEn is relatively insensitive to noise and leads to more reliable estimations. Also, DisEn is simple and computationally fast as PerEn, so it is suitable for applications of real-time monitoring. Moreover, the mean value of signal amplitudes and differences between amplitude values are given consideration in DisEn. [PERSON] et al. [24] combined DisEn and EMD to extract the rolling bearing fault feature. In [25], DisEn was calculated with the morphology envelope of the original signal for multi-fault recognition of rolling bearings. To enhance the stability of multiscale DisEn [26], [PERSON] et al. [27] further proposed an improved multiscale DisEn for fault diagnosis of rotating machinery. In view of the facts mentioned above, nonlinear entropy can be good symptom features for fault diagnosis of rolling bearings. After the extraction of fault features, fault classification is essentially a problem of pattern recognition and is often intelligently accomplished with some kind of machine learning models. In [28], the rolling element bearing fault was diagnosed using a hidden Markov model with the extracted singular features. [PERSON] et al. [29] detected bearing malfunction with a probabilistic neural network and then categorized bearing faulty states with a simplified fuzzy adaptive resonance theory map. [PERSON] et al. [30] studied comparisons of four representative fuzzy clustering algorithms for bearing fault diagnosis. A sparse autoencoder was proposed in [31] both for feature extraction and fault classification of rolling bearing. Support vector machine (SVM) [32] based on structural risk minimization is a promising classification method for small samples and has been successfully applied to many engineering fields, such as remote sensing, credit scoring, mechanical fault diagnosis, etc. [33; 34; 35]. However, many SVMs should be combined for a multi-class classification since SVM is designed for binary classification problem. Secondly, SVM learning for big data applications is of low efficiency. To overcome these issues, the ensemble SVM (ESVM) was proposed and has been verified with better classification performance than a single SVM for multi-pattern recognition [36; 37; 38]. ESVM is a collection of SVMs, and the classification results of these SVMs are combined in some way to output the final decision; thus, the classifying generalization performance is to be improved by utilizing the difference of each individual SVM. Through the above literature analysis, a novel method for multi-fault diagnosis based on ICEEMDAN, nonlinear dynamics entropy, and ensemble SVM is presented for rolling bearings. In the proposed method, the nonstationary bearing vibration signal is decomposed by ICEEMDAN, and three kinds of entropy features are extracted from each of the obtained IMFs. Then, a distance-based method is formulated for selecting discriminative features, and a multi-class ESVM is constructed and trained for intelligent classification of multi-fault rolling bearings.