# Research on Intelligent Fault Diagnosis of Rolling Bearing Based on Improved Deep Residual Network
## 1 Introduction  
Hydropower Units, Large wind power equipment, and other rotating machinery are developing towards high precision machinery field. A reliable health detecting system is key for the steady operation of mechanical equipment [1]. Rolling bearing affects the overall performance of Rotating machinery [2]. Fault diagnosis of rolling bearings has attracted more and more attention. It can minimize maintenance costs and increase system reliability [3]. Rolling bearing fault diagnosis is mainly to extract features from vibration signals that was collected, and to identify and classify faults. The fault diagnosis methods based on signal processing have been widely studied. [PERSON], et al. developed a fault diagnosis method based on the Gaussian correlation of vibration signals and wavelet coefficients for gear [4]. [PERSON], Gaigai CAI put forward fault diagnosis modus based on sparse representation in wavelet basis for gearbox and it is superior to empirical mode decomposition (EMD) especially in transient feature extraction [5]. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] proposed a fault diagnosis scheme based on a binary bat algorithm (BBA), that is superior to other dimensionality [6]. [PERSON], [PERSON], et al. uses supervised extended local tangent space alignment (SE-LTSA) for dimensionality reduction to make better the effectiveness of fault diagnosis in rotating machinery [7]. [PERSON], [PERSON], et al. put forward a new morphological component analysis (MCA) method used for the complex fault diagnosis of gearboxes [8]. [PERSON] examines a method based on vibration analysis for diagnosis of distributed bearing faults [9]. [PERSON], [PERSON], et al. proposed a new vibration analysis method based on the adaptive local iterative filtering used for a hydropower unit [10]. [PERSON], [PERSON], et al. presented a novel method based on linear transformation of intrinsic time-scale decomposition (ITD)and the cubic spline interpolation. Also, this method was used for diagnosing wind turbine faults with decomposing nonstationary vibration signal, that it did identify wind turbine gearbox fault. The method can solve the problem that is recognizing fault conditions when two or more fractal dimensions are close to each other [11]. From these features, fault detection and classification can be done through various machine learning techniques [12]. A support vector machine (SVM) is to detect bearing faults using harmonics of fault-related frequencies from vibration signals [13]. It also involved an ANN for bearing faults using a genetic algorithm [14]. Support vector machine (SVM) and Artificial neural network (ANN) are the two most popular intelligent diagnosis methods. Although traditional intelligent diagnosis methods have made a great contribution to rolling element-bearing fault diagnosis, they still have some shortcomings. For example, feature extraction or selection relies heavily on expert knowledge and extensive human labor [15]. Furthermore, Artificial intelligence methods, such as SVM and ANN are shallow learning models. It is difficult for the shallow learning model to learn complex nonlinear relationships effectively [16; 17; 18]. Hence, deep learning has been investigated for automatic and effective fault feature learning of rolling-element bearing in recent years. Deep learning is no longer a new concept [19]. At first, deep learning was used in image processing, audio processing, and other related fields, and has achieved great success [20; 21]. Therefore, researchers have introduced deep learning into fault diagnosis. [PERSON], [PERSON], et al. proposed a new data-driven remaining useful life (RUL) estimation approaches of bearings based on Deep Spatiotemporal Convolutional-Neural-Network [22]. [PERSON], [PERSON], et al. applied deep neural networks to conquer the flaw of the before-mentioned intelligent diagnosis methods [23]. [PERSON], [PERSON], et al.