# Review of Vibration-Based Structural Health Monitoring Using Deep Learning
## 2 Early Health Monitoring Using Vibration Responses  
Many attempts have been made to monitor faults or the health condition of a dynamic system by using deviations in the vibration characteristics. In order to diagnose the health condition of a machine, researchers have used signal processing techniques to analyze the vibration response, such as fast Fourier transform [18; 19; 20; 21], envelope [22; 23; 24], cepstrum [25; 26; 27; 28; 29] and wavelet [30; 31; 32; 33] analysis. The processed signals provided information on the spectral response of a machine. Before deep learning was widely applied to health monitoring, conventional classifiers such as the artificial neural network (ANN) [34; 35; 36; 37; 38], support vector machines (SVMs) [39; 40; 41; 42], principal component analysis (PCA) [43; 44; 45; 46] and k-means [47; 48; 49; 50] algorithm used the processed data as features. [PERSON] et al. [51] studied the aging and degradation of a bridge. To carry out an ambient vibration test, twelve force balance accelerometers were installed inside the deck box girder to measure the lateral and vertical accelerations. They monitored the time evolution of the natural frequency with the surrounding temperature, natural frequency variation with the RMS values of the vertical acceleration, average day evolution of the modal damping ratios and natural frequency change during the working days to observe possible changes in the status of the bridge. Regression analysis and PCA permit the detection of very small frequency shifts. [PERSON] et al. [52] used the frequency response to detect damage in composite materials. They manufactured composite specimens with holes, reduced modulus and delamination and used a laser vibrometer to measure vibrations. Each specimen was adhered to two lead zirconate titanate (PZT) wafers for excitation. The extent of damage and reduction in natural frequency strongly corresponded to the clamped composite plates. The finite element model (FEM) has been used to compare the frequency responses in experiments. To identify the damage to a structure, many numbers and types of sensors have been used. [PERSON] et al. [53] used operational deflection shapes (ODSs), which visualize the vibration displacement or velocity of a structure that is excited by a specific structural loading. They tested two aluminum beams in the healthy and damaged cases. The prepared a scanning laser Doppler vibrometer (SLDV) to measure the vibration and mounted a mass actuator on the beam. FEM simulations showed that the damage localization was more accurate at a higher frequency range. Neural networks have been applied to health monitoring in many cases [54; 55; 56; 57; 58; 59; 60]. [PERSON] et al. [61] successfully applied the wavelet transform (using D4 orthogonal wavelets) to preprocessing six different types of vibration signals from a model drive-line. ANNs are inspired by the thought process of the human brain. Neurons are interconnected to each other in the brain, and nodes serve the same role in an ANN. ANNs have been used to determine different kinds of faults and differentiate between single and multiple faults. [PERSON] et al. [62] vibrated a structure with ultrasound-guided waves and diagnosed the integrity of a structure by using an unsupervised machine learning algorithm and physics-based temperature compensation. They used PZT to transmit and record guided waves on a thin aluminum plate. Fatigue was generated by a crack along the edge of a center hole. A Tonetone-burst signal was generated by PZT transducers at a center frequency of 250 kHz. Unsupervised ANN was used to detect the damage. A temperature compensation was implemented based on physical insight. They showed that the influence of applied loads needs to be filtered [63]. [PERSON] and [PERSON] [64] used Lamb waves to localize damage. They attached a carbon/epoxy resin plate to PZT to serve as transducer and receiver. They performed a discrete wavelet transform (DWT) to determine the time delay due to damage to the plate.