# Review of Vibration-Based Structural Health Monitoring Using Deep Learning
## 6 Power Source-Induced Vibration  
Research on using deep learning in the health monitoring of moving parts in transportation systems, plants and manufacturing processes is increasing. [PERSON] et al. [97] measured the vibrations of hydraulic cylinders on steel platforms to monitor five states: normal, blockage, leakage, cavitation and impulsion. The vibration signal of the hydraulic system provided sufficient state information for fault diagnosis with the sliding window spectrum feature (SWSF) of a deep belief network (DBN). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Technique** & **Advantages** & **Disadvantages** \\ \hline \multirow{2}{*}{Autoencoder} & Easy to implement & Requiring a huge amount of data for training \\  & Easy to track the loss/cost & Requiring irrelevant information for learning \\ \hline \multirow{2}{*}{DBN} & Good to analyze one-dimensional data & \multirow{2}{*}{Inefficient and very slow} \\  & Possible to extract the global feature from raw data & \\ \hline \multirow{2}{*}{CNN} & Good to analyze multidimensional data & Complicated, with network structure \\  & Specialized to extract local feature & requiring a lot of calculating time \\ \hline \multirow{2}{*}{RNN} & Outperforming with sequential data & \multirow{2}{*}{Difficult to implement and train} \\  & Good to calculate the feature from time variations & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of deep learning techniques. The feature extraction was fast and effective with the SWSF. The DBN allowed a large amount of data with redundancy and nonlinearity to be processed. [PERSON] et al. [98] used an accelerometer to measure the vibration at the end face of an axial piston hydraulic pump to compare two conditions: piston shoes and swashplate (PS) and valve plate (VP) wear. [PERSON] et al. used a convolutional neural network (CNN), which could easily distinguish grayscale images by using the transient variation of the vibration response to represent a pixel value, as shown in Figure 4. It can be seen that the pattern of normal state and the pattern of failure are different. They compared the results with the speeded up robust features (SURF)-based PNN carried out in [99] and found that their method outperformed. Two types of vibration occurred during stationary motion, and the spectral characteristics needed to be analyzed. [PERSON] and [PERSON] [100] applied a deep learning algorithm to a large gas engine that experienced abnormal vibration due to knocking. Knocking is an abnormal engine combustion process that should be prevented. Vibration was applied for a short time and monitored. They analyzed the chaoticity of the knocking by applying the DBN to distinguishing between the vibration caused by the valve and combustion during engine operation and the vibration caused by knocking. [PERSON] et al. [101] detected the impact signal generated by defects to predict the failure of a machine during the production process, as shown in Figure 5. To solve the inverse problem, denoising, deconvolution and interpolation are used [102]. In the presented flow chart, deep learning is defined by the BPNN layer in step 4. If GA and MCI are used instead of BPNN, they can be defined as general inverse problems and SVM, PCA and k-means can be defined as machine learning. Deep learning was applied to analyze the dynamic characteristics. The impulse responses were difficult to analyze because of the harmonics and noise generated during the machining process. A deep autoencoder was used to distinguish between the normal vibration response and the impact vibration due to defects; 288 days of the transient vibration signal were used for learning and verification. [PERSON] and [PERSON] [103] used the imbalance data of structure-borne noise from engine excitation to predict damage to the engine without measuring the vibration of the engine. The measured transient responses were transformed into the frequency domain with the Fourier transform. The 1D CNN was applied to the sequence of frequency values.