# Review of Vibration-Based Structural Health Monitoring Using Deep Learning
## 8 Ambient Excitation  
Until now, this paper has introduced machine systems that move during health monitoring. This section introduces health monitoring methods that utilize the vibration characteristics of the target system caused by the external environment of the mechanical system. The fiber recognition system is a hybrid fiberoptic acoustic sensor (DAS) that is used in various fields. Machine learning methods that use features extracted manually by professional knowledge are commonly applied to data that change at times. [PERSON] et al. [132] tried to apply deep learning to recognizing the DAS signal. The background noise was mainly measured in the basement of the farm through which the pipe with the cable passed, but there was some interference from country lanes. Traffic noise passing through the ground was mainly collected, and the noise level of the surrounding factory was measured as shown in Figure 8. Regular impulse signals from the forging plant or small but continuous vibration of the fabricating plant or sand washing plant were measured. To measure this external excitation, CNN was introduced, and its performance was compared with other discrimination methods, as shown in Figure 9. They did not consider complex excitation sources for the pipe, but each noise source was measured with high accuracy. [PERSON] et al. [133] applied deep learning to diagnosing the condition of a wind-driven machine like wind turbines. The experimental platform consisted of a wind tunnel, direct drive wind turbine test bench, accumulator and DAQ system. The experiment was conducted on 10 defects. The performance of the CNN layer was varied, and the deep adversarial convolutional neural network (DACNN), which is the deep learning model of the CNN combined with the generative adversarial network (GAN), was applied to the discrimination. [PERSON] et al. [134] used deep learning to distinguish between coal and rock in the coal mining process. The coal is moved after being mined and falls on the belt, which can be used to examine the different vibrations and sounds of coal and stones. They improved the accuracy by discriminating between the signals generated by accelerometers and microphones attached to the belt individually or with both at the same time. [PERSON] et al. [135] performed aircraft wing structure diagnosis and DBN-based health incident classification to determine the engine condition and compared the performance with SVM. Aircraft wings are exposed to large changes in temperature and humidity and to a high-speed and high-pressure environment, so there is a high risk of breakage. During flight, the wings vibrate because the airspeed is low. The airspeed is low, and the airspeed is low. of wind; this was measured with a vibration sensor attached to the wing. They applied DBN to the vibration signal for fault classification. [PERSON] et al. [136] proposed using deep learning for an automated fault detection method to maintain tidal power equipment. Vibration data were gathered from a tidal turbine's nacelle. By improving the performance of feature-based methods, their network classified ideal and fault conditions with 100% accuracy. The fault diagnosis of bogie vibration data is important, but the error mechanism is very complicated and the signal characteristics are not clear, so applying traditional signal processing methods is difficult. [PERSON] et al. [137] applied deep learning to diagnose faults in bogies. With knowledge and diagnostic experience, deep learning is no longer needed to solve this problem. At different speeds, each fault of high-speed trains was diagnosed with very high discrimination accuracy. [PERSON] et al. [138] applied deep learning to the small fault diagnosis of the front-end speed controlled wind generator (FSCWG) for which early diagnosis is difficult owing to the time-varying and nonstationary characteristics. The vibration signal was measured at the wind turbine of the wind farm.