# Review of Vibration-Based Structural Health Monitoring Using Deep Learning
## 7 Vibration of a Rotating Object  
Many research groups have applied deep learning to rotating systems because of their significance to securing the safety of industrial construction. For rotating parts, directly monitoring the vibration response is not a straightforward process because of the difficulties with installing equipment and delivering the measured signal. An indirect method is required to monitor the vibration of rotating objects based on those of the supporting structure. The training availability and convergence ability increase when a huge amount of data is available. The rotation signal fits very well to the characteristics of deep learning. In the case of a rotating system used in a machine, different signals of the same state can be generated in a relatively short time. It is advantageous that the rotating speed is easily detected in the frequency domain. The important mechanical components for rotation are gears and bearings. This section introduces cases where deep learning was applied to diagnosing gears, bearings, their accessories and attached systems. [PERSON] et al. [108] applied a novel diagnosis method of applying a stacked sparse denoising autoencoder (SSDAE) to a rolling bearing. The experimental stand consisted of a fan end bearing, electronic motor, drive end bearing, torque transducer and dynamometer, as shown in Figure 6. At Case Western Reserve University (CWRU), [PERSON] and [PERSON] [109] carried out an experiment where the nonlinear and nonstationary vibrational signals were decomposed by ensemble empirical mode decomposition (EEMD). As shown in Figure 7, four types of health conditions with three fault datasets were used to validate the performance of the proposed method and demonstrate its effectiveness compared with the other stack form. [PERSON] et al. [110] applied various types of deep learning to the spur gear to compare their performances at monitoring failure. Several types of tooth breakage were measured. [PERSON] et al. [111] used deep learning to diagnose the failure of a transmission chain. They verified the method using the dataset of the bearing and the gearbox and classified it with a very high accuracy. [PERSON] et al. [112] proposed deep convolutional neural networks with wide first-layer kernels (WDCNN), which use a wide kernel in the first layer to suppress high-frequency noise and extract features. Small kernel was used for multilayer nonlinear mapping. Horizontal flips, random crops/scales and color jitter are widely used for data enhancement; the training samples were augmented by cutting and overlapping data. They used the proposed method to diagnose faults in a motor mechanical system. Figure 6: Bearing test ring tests from [108], Copyright 2019, MDPI. A = fan end bearing; B = electronic motor; C = drive end bearing; D = torque transducer; E = dynamometer. [PERSON] et al. [113] studied how to overcome the difficulty of applying a deep learning model that was constructed with a small change in the measured trained vibration responses or when the load changed during actual application. Transfer learning solves these problems under robust work conditions. The constructed training dataset should be trained again when a new vibration signal is encountered. Therefore, they replaced the training of new target datasets with adaptive batch normalization (AdaBN), which has three steps: processing the vibration response into regular and differential frequency spectra, applying a DNN in advance with a stacked autoencoder (SAE) and modifying a trained network to diagnose a sample of the target dataset. They applied this method to find faults in gears and bearings. There have been numerous cases in which various feature extraction methods and deep learning with different layers were applied to the fault diagnosis of gears and bearings [114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124], which have been summarized in several reviews [125, 126, 127]. The condition of flying vehicles needs to be predicted for maintenance and early warning of failure. [PERSON] et al.