# Rolling Bearing Fault Diagnosis Based on CEEMDAN and CNN-SVM
## 1 Introduction  
With the development of modern science and technology, all kinds of mechanical equipment tend to be large-scale and complicated, thus increasing the uncertainty of the safety of mechanical equipment [1]. In the field of industrial production, rotating machinery and equipment is a kind of extremely important machinery and equipment, playing the role of supporting and reducing the friction of the unit [2, 3]. Rolling bearings are the core components of rotating machinery and equipment, and due to long-term high-speed rotation, easily produce all kinds of damages. These damages may cause excessive energy consumption and mechanical fatigue of the equipment, or cause major safety accidents [4, 5]. Therefore, effective fault diagnosis of rolling bearings is essential to ensure the safe operation of production equipment and the safety of people's lives and property. At present, the rolling bearing fault diagnosis method based on vibration signal is the mainstream method [6]. However, due to the complex working environment of rolling bearings, the collected vibration signals are interfered by external noise, which makes it difficult to extract fault characteristics. Therefore, the vibration signal is usually preprocessed first. Commonly used methods include Fourier transform, wavelet transform, empirical mode decomposition (EMD), etc. [PERSON] et al. proposed the EMD algorithm, which is completed by decomposing the original signals step-by-step and generating some intrinsic mode functions (IMF) [7]; however, the EMD has very serious mode aliasing and endpoint effects [8]. [PERSON] and others proposed the ensemble empirical mode decomposition (EEMD) [9], whichadds white noise to the original signal, then decomposes the noise-added signal using the EMD algorithm and finally averages the results of multiple decompositions, which effectively suppresses the phenomenon of modal aliasing. However, the EEMD decomposition method has noise residuals, which leads to reconstruction errors [10]. [PERSON] et al. improved the EEMD algorithm and proposed a new signal-processing algorithm of complete ensemble empirical model decomposition with adaptive noise (CEEMDAN) [11; 12]. The CEEMDAN algorithm effectively solves the deficiencies of EMD and EEMD, and is a very effective decomposition algorithm [13]. When traditional machine learning algorithms are applied to the field of fault diagnosis, expert knowledge and experience are required, and the extracted features are affected by human factors [14]. With the development of deep learning, convolutional neural networks have shown significant advantages in the field of fault diagnosis by virtue of their powerful feature extraction capability. CNNs are able to automatically extract and identify key features related to faults by learning the features in the data by themselves, which has led to a significant improvement in the accuracy and efficiency of fault diagnosis [15; 16]. [PERSON] et al. proposed a bearing fault diagnosis technique based on parameter-optimised variational pattern extraction and an improved one-dimensional convolutional neural network [17]. [PERSON] et al. proposed an attention-based ConvNeXt with parallel multiscale dilated convolution for the fault diagnosis of rotating machinery [18]. [PERSON] et al. proposed a rolling bearing fault diagnosis method based on vibration image coding and a multi-scale neural network [19]. However, traditional CNN for fault diagnosis usually uses the Softmax classifier to achieve fault classification, but the SVM classifier has higher accuracy than the Softmax classifier in multi-classification. Aiming at the problem that the bearing vibration signal is affected by noise which leads to the difficulty of fault feature extraction, a fault diagnosis method based on CEEMDAN and CNN-SVM is proposed in this paper, which is completed as follows: Firstly, the original vibration signal is decomposed using the CEEMDAN algorithm to obtain a series of IMF components.