# Review of Vibration-Based Structural Health Monitoring Using Deep Learning
## 1 Introduction  
Vibration originates from many different sources, including fluid flow, magnetic fields, translating and rotating elements, imbalances, interactions and frictional contacts. The vibration mode depends on the structural properties, geometry and boundary conditions, which may change with time. Consequently, the transient variation of the vibration response shows large statistical deviations. Despite the difficulties with interpretation of the measured values, the response provides important information about the status of a structure. Advances in engineering technology have increased the size and complexity of structures. To minimize the costs induced by unexpected failure, the need for continuous structural health monitoring is increasing. Using vibration to monitor structural integrity allows for robust, efficient and straightforward implementation. However, using vibration for fault inspection requires understanding of multidisciplinary research fields. According to [PERSON] and [PERSON] [1], the fundamental issue with structural health monitoring is to identify whether a system has departed from the normal state. [PERSON] [2] proposed a four-level method to identify damage: (1) determination of the existence of damage to the structure; (2) determination of the location of the damage in the structure; (3) quantification of the degree of damage; (4) estimation of the life expectancy of a structure influenced by damage. Several attempts have been made to apply machine learning to structural health monitoring. Rapid advances have led to the application of deep learning [3] for vibration-based health diagnosis. [PERSON] and [PERSON] [4] and [PERSON] and [PERSON] [5] used Fourier and wavelet transforms of the vibration signal to detect defects in gearbox and induction machines. [PERSON] et al. [6] investigated the efficiency of selecting features and usage as learning patterns for inspection. Data-driven methods have been used for the fault diagnosis of complex engineering systems. They require little understanding of the kinematics and physics of the tested system [7]. This data-driven approach requires two steps:feature extraction and fault classifier design. Time, frequency and time-frequency analysis is widely used to extract features of the vibration responses of a rotating machine. This method provides a very accurate diagnostic performance at the laboratory scale. This high level of accuracy depends on the physical properties, size or position when applied to actual operating systems. The vibration was also used to monitor the health of composite laminate structures. The natural frequency, damping, mode shapes curvature, transfer function and frequency response are used to analyze the transient responses. Defects have been diagnosed by using the time of flight for the propagation of a high-frequency guided wave transmitted to a structure. Low-frequency vibration has disadvantages for localization because the entire structure is excited [8]. High-frequency surface wave propagation can be utilized for precise measurement, but the requirement of a high-voltage power source increases the costs of the monitoring system. Inverse analysis method was proposed to analyze the fracture of structures. The lifespan of the structure decreases with the growing crack. Inverse problems were used to estimate the location of an unknown damage in a structure [9; 10]. [PERSON] et al. [11; 12; 13; 14; 15] proposed an optimal technique by applying an inverse problem to FEM. [PERSON] et al. [16] used frequency response functions (FRF) to find the position and quantity of damage in truss structure based on genetic algorithm (GA) and bat algorithm (BA). The algorithms with better accuracy and computational time were identified. [PERSON] et al. [17] modified the Cornwell indicator (CI). This modified Cornwell indicator (MCI) improved in accuracy and localization compared to the CI. Design of a fault classifier requires condition monitoring for offline training.