# Review of Vibration-Based Structural Health Monitoring Using Deep Learning
## 10 Conclusions  
Table 2 summarizes the research using deep learning for structural health monitoring that were referenced in this review. A combination of various signal preprocessing methods and deep learning architectures is being studied for vibration-based monitoring. Structural health monitoring using vibration response has the elements necessary for classification by deep learning, such as a clear distinction between normal and abnormal status. Data processing can be used for data augmentation. In addition to ongoing developments in researches of deep learning, various and effective condition diagnostics are expected to be proposed. Figure 11: Identification schematic using a convolutional neural network from [140], Copyright 2018, MDPI. [PERSON] et al. [15] presented a damage indicator based on normalized modal strain energy indicator (nMSEDI) and compared the performance of the local frequencies change ratio (LFCR). Computational (CPU) time of LFCR based on FEM was about 60 s and nMSEDI took about 0.28 s. [PERSON] et al. [132] used 1-D CNN + SVM method with 358 samples per second processing speed. Inverse analysis has the advantage of short computation time because the amount of data calculation is smaller than that of deep learning. However, deep learning has the advantage of being able to perform health monitoring on complex structures with high accuracy. Advances in machine learning technology have rapidly increased its application in vibration-based structural health monitoring, as summarized in this review. Since AlexNet's success in 2012, interest in deep learning has been increasing rapidly. Based on this trend, research on the possibility of applying deep learning to structural health monitoring has also increased. A database search showed that the number of works reported in the area of structural health monitoring with deep learning has been increasing each year from 279 in 2012 to 323, 402, 440, 433, 524 and 661 in 2013-2018, respectively. A further increase is expected in 2019. The use of structural vibrations for fault diagnosis is advantageous for intrinsic diagnosis of the installation responses. For practical applications, issues related to nonlinearity, nonstationarity and time variation should be resolved. Deep-learning-based algorithms commonly utilize big data. Consequently, they are advantageous in terms of robustness. The amount of data used for discrimination is large to filter out unnecessary information. However, it is important to ensure converged results and to overcome the problem of an aperiodic abnormality signal being generated. Studies are actively ongoing to use data augmentation or directly applying vibration to a system to detect an abnormal signal. The vibration responses are obtained in the time domain. The raw data were used for categorization and feature extraction. Signal processing (e.g., Fourier transform, Hilbert transform and wavelet transform) was also used. Deep learning is a multilayered neural network that can be fabricated in various ways, depending on how the layer is constructed. The deep learning algorithms established their own features that characterize the subject. Production of high-accuracy results is expected in a fast and efficient manner. Therefore, the use of deep learning to analyze unstructured data such as voice, video, photo and sensor measurements is increasing.