# Bearing Fault Diagnosis Based on Improved Convolutional Deep Belief Network
## 1 Introduction  
With the explosive progress of modern science and industry, machinery and equipment in fields such as aerospace, rail, and wind power are becoming faster, more automated, and meticulous than before. However, increasingly complex operating conditions inevitably lead to failures. Thus, monitoring and diagnosing the health of rotating machinery and equipment to ensure operational safety and reliability are immensely necessary and urgent; fortunately, intelligent diagnosis approaches, the combination of classification algorithms, and signal processing techniques have produced promising results [1]. Based on vibration signal processing, the traditional fault diagnosis methods extract fault components from the initial noise signal by using rich professional knowledge. At present, signal processing methods, such as empirical mode decomposition (EMD) [2], wavelet packet transform [3],and morphological filter [4], are commonly used for time-frequency analysis. For separating bearing fault ingredients in a wind turbine gearbox, [PERSON] et al. [5] applied improved convolutional neural network with anti-interference to rolling bearing performance degradation assessment. [PERSON] et al. [6] proposed a novel deep learning method called improved convolutional neural networks and support vector machines with data fusion for intelligent fault diagnosis. To realize bearing fault detection, [PERSON] et al. [7] formed a quantity matrix of bearing vibration signal features on the basis of EMD and local mean decomposition. In recent years, machine learning methods, such as support vector machines [8] and artificial neural networks (ANNs) [9], have been gradually introduced into mechanical fault diagnosis. Such an introduction has enabled the minimization of human intervention and reliance on professional skills for development toward an intelligent direction in this field. As an urgent field in machine learning research, deep learning has begun to appear in bearing failure in recent years, including stacked autoencoder (SAE) [10], deep belief networks (DBNs) [11], and convolutional neural networks (CNNs) [12]. [PERSON] et al. [13] summarized the emerging research on machine health monitoring based on deep learning, then discussed new trends in machine learning monitoring methods of deep learning. In considering ways of enhancing the robust features for rotating machinery, [PERSON] et al. [14] proposed an automatic learning method with robust features based on contractive autoencoder. [PERSON] et al. [12] proposed cyclic spectral coherence and convolutional neural networks for bearing fault diagnosis. [PERSON] et al. [15] compressed the data with autoencoders and constructed a convolutional deep belief network (CDBN) to diagnose bearing faults. In view of compressed sensing and deep learning theory, [PERSON] [16] studied a fault diagnosis method of bearing vibration signals that can extract features automatically. In summary, traditional methods have several shortcomings, as follows:  
1. Traditional vibration signal processing and analysis methods rely on certain professional skills;
2. Existing shallow machine learning methods rely on the accuracy of manual feature extraction;
3. Improper selection of parameters for standard deep learning models can easily result in failure to effectively converge, thus, diagnostic accuracy is difficult to guarantee;
4. Existing research on the quantitative diagnosis of bearing fault is relatively inadequate compared with that on qualitative diagnosis. It can be concluded that the general deep models have challenges: (1) a suitable signal preprocessing method is needed to enhance features; (2) necessary measures need to be taken during the model training process to make it more stable; (3) the models should determine not only the fault type, but also the fault degree. In response to these problems, a CDBN has the superiority of fast calculations and feature extraction.