# Review of Vibration-Based Structural Health Monitoring Using Deep Learning
## 6 Power Source-Induced Vibration
The magnitude of the vibration due to engine failure was relatively small compared to the normal signal. These signal characteristics induced an imbalance in the labeling of data during the training process. There is a method of fitting a large amount of data or weighting to a small number of data. They applied two methods to the same dataset. [PERSON] et al. [104] used the generated sound for effective labeling to evaluate the quality of the vehicle interior noise. To record the interior noise, they set up a microphone above the driver's seat. The internal noise of the vehicle was analyzed with the discrete wavelet transform (DWT) to derive eight sound quality factors (sound pressure level, A-weighted sound pressure level, loudness, sharpness, roughness, fluctuation strength, articulation index and tonality) under the idle and running conditions. Prediction accuracy was compared by evaluation of their method with using multiple linear regression (MLR), back-propagation neural network (BPNN), general regression neural network (GRNN) and SVM. Figure 4: Converted images on nine fault conditions from [98], Copyright 2018, IEEE. In addition to signal measurement, deep learning has also been proposed for condition diagnosis. [PERSON] et al. [105] utilized the speed variation to identify the vibration status of the locomotive absorber on a high-speed train. Knowing whether the fault features are stable or restricted by the number of samples is a difficult practical problem for approaches based on dynamic models. For high-speed trains, securing enough training data for deep learning models is difficult. To distinguish 208 actual cases of speed data, they generated fourteen thousand input situations. Although the actual measurement accuracy was not very high, they realized a much higher accuracy than with backpropagation (BP) or SVM. [PERSON] [106] solved a similar problem for helicopter vibration. Numerical modeling was performed using a baseline model and ReLU networks because of the difficulty with securing enough input properties for applying deep learning to differentiating the vibration response of the helicopter to various states. The proposed model was compared with those from the Stanford Autonomous Helicopter Project [107]. Experience has shown that the acceleration of the helicopter in the up and down directions was the most difficult to predict. Thus, they focused on the vertical acceleration of the helicopter. The model provided more precise predictions than the linear acceleration model, linear lag model and quadratic lag model. The acceleration due to the flight state was nonlinear, which made the prediction with conventional models difficult. Deep learning models are appropriate for such nonlinear systems. So far, cases where deep learning was applied to the discrimination, prediction, and diagnosis of moving mechanical systems have been examined. By measuring the dynamic characteristics of the system according to its movement based on vibration and sound, features can be extracted by various methods and applied to deep learning to distinguish the desired response or between the abnormal and normal responses at the same time. Various attempts have been made in vibration signal analysis to combine signal processing methods with deep learning to outperform conventional discrimination methods. Figure 5: Framework of the early fault detection method based on deep learning and dynamics identification from [101], Copyright 2019, IEEE.