# A Survey on Fault Diagnosis of Rolling Bearings
## 4 Rolling Bearing Fault Type Recognition
### Deep Learning Based Fault Type Recognition Methods  
Deep learning techniques have strong learnability. By stacking non-linear processing units layer by layer, it can automatically learn effective features from the raw data without manual feature extraction and manipulation. Deep learning methods are primarily implemented based on ANN, including convolutional neural networks (CNNs), Autoencoder (AE), deep belief networks (DBNs), and recursive neural networks (RNNs). Deep learning methods were used to address machine vision, image processing, speech recognition, text analysis, and other problems. Inspired by these successful applications, deep learning methods have been gradually introduced into the field of fault diagnosis over the past five years [144]. CNNs is the most commonly used deep learning method for fault diagnosis. The network structure is usually composed of convolution layers and pooling layers. The convolution layer convolutes with the original input data to obtain shallow features, and then the pooling layer captures the relatively important features through down sampling. The deep characteristics of the data are gradually obtained by alternately stacking the convolution layer and the pooling layer. CNNs were first used to identify the fault types of rolling bearings in 2016 [145], and then it was widely used and improved [146; 147; 148]. The input data of a CNN can be one-dimensional bearing vibration signals or two-dimensional images (i.e., spectrogram, texture, and grayscale) converted from one-dimensional vibration signals. Accordingly, 1D-CNNs and 2D-CNNs methods were developed. [PERSON] et al. [146] transformed the one-dimensional time series signals into two-dimensional image signals through random sampling segments of the original signals and fed these images into Lenet-5, which achieved satisfactory results in three different mechanical fault diagnosis tasks. In [147], [PERSON] et al. applied Morlet wavelet decomposition and bilinear interpolation to convert the vibration signal into grayscale images and then used rectified linear units and the appropriate dropout strategy to improve the generalization performance of CNNs for fault diagnosis. [PERSON] et al. [148] proposed an improved CNN model using the original vibration signals as inputs. This method uses a wide convolution kernel for extracting features and suppressing high-frequency noise and small convolutional kernels in the preceding layers for performing multilayer nonlinear mapping. The CNN-based fault recognition methods typically extract the internal features of bearing signals through multiple convolution layers and pooling layers and perform fault type recognition by using the fully connected layer, which has a layer with Softmax or Sigmoid function for classification, or using other classifiers, such as KNN, to perform classification  
AE is a special neural network that consists of two parts, i.e., encoding and decoding, which is to reconstruct input data for obtaining the discriminative data information. The use of improved AE methods has enhanced the processing performance of fault diagnosis. For example, the denoising AE method was proposed by adding noise to the original data, the sparsity AE method was implemented by introducing sparse constraints to the output layer, and the stacking AE method was developed by combining multiple AEs. In [149; 150; 151], AE and its improved versions were utilized for extracting discriminative features from the original vibration of signals, based on which bearing fault types may be accurately recognized. [PERSON] et al. [149] used AE to fuse the extracted features of the bearing signals, thereby reducing the redundancy of signals. [PERSON] et al. [150] developed the sparsity AE by adding a sparse penalty to AE for high-level feature learning and bearing fault recognition. [PERSON] et al. [151] proposed a novel diagnosis method based on Teager computed order spectrum and stacking AE.