# A Review of Deep Learning Applications for Railway Safety
## 5 Train Safety
### Bearing  
Bearing is a principal component widely used in most modern mechanical equipment. Defect inspection of bearings takes a long time and the cost of repairs is generally high, which can significantly decrease train productivity. While there have been many attempts to detect bearing defects, conventional methods have two limitations. First, methods based on features depending on expert rules or prior knowledge take too much time and human effort because different processes conducted by experts should be performed according to each specific problem. Second, traditional machine learning methods with shallow structures have had difficulty estimating nonlinear functional relationships in complex systems. In order to overcome these limitations, there have been studies to adopt deep learning to detect bearing defects. [PERSON] et al. [72] proposed a CNN-based model for bearing defect detection. Their proposed model used bearing vibration signals for defect detection. It converts original signals into two-dimensional features by CWT. Then, a CNN based on LeNet-5 processes features and determines its state. In addition, an ensemble method was adopted to utilize three Random Forest (RF) models with features of three specific layers as input values. [PERSON] et al. [73] have developed a deep learning model that diagnoses defects using the Large Memory Storage and Retrieval Neural Network (LAMSTAR). This multi-layer fast deep learning structure can use many filters simultaneously. In addition, the short-time Fourier transform (STFT) is used to process acoustic data generated from bearings to determine when signals for each frequency band separated from the composite signal are generated. Performance tests performed in laboratory environments showed better performance than other conventional CNN models. The features of bearing vibration signals, such as high nonlinearity, non-stationarity, and background noise, make it hard to diagnose bearing faults effectively and accurately. [PERSON] et al. [74] proposed a deep learning method based on discrete wavelet transform (DWT) and improved DBN. First, the vibration signals from faulty bearings were converted to a two-dimensional (2D) time-frequency map. Then, the time-frequency map was processed by an improved DBN model, aiming to identify the correlation between fault features and fault types. In this way, the fault state of the bearing in the traction motor was diagnosed and identified in a semi-supervised manner. Figure 7 shows examples of railway equipment detection. [PERSON] et al. [75] proposed a model that utilizes Faster R-CNN to detect the location of the target component and whether it is defective from a complex background in a bogie image. In particular, they improved the original faster R-CNN by using two layers of different sizes for extracting defect regions and enabling region of interest (ROI) pooling. Experiments on 6499 test data on four parts (cut-out cock handle, dust collector, fastening bolts, and bogie block key) showed high detection accuracy with fast speed. [PERSON] et al. [76] proposed a CNN model that detects defects in the side frame key (SFK) and shaft bolt (SB) among bogie components. The detection model accurately located the SFK and SB from the Trouble of Running Freight Train Detection System (TFDS) image data and then cropped it to diagnose each defect. [PERSON] et al. [77] proposed a hierarchical feature-based instance detection (HID) model to detect lost or broken defects in bogie components. Their proposed model consisted of three modules. The first module extracts hierarchical image features from train images through a CNN model. The second module delivers the extracted feature map to the region proposal network to generate a defect object area. The last module finally detects defects based on the generated regions and the feature maps. The proposed instance-level detection was evaluated on six train defects (lost pin, lost bolt, lost rivet, foreign object, broken chain, and broken wire). [PERSON] et al.