# Review of Vibration-Based Structural Health Monitoring Using Deep Learning
## 7 Vibration of a Rotating Object
[128] simulated engine operation in an aircraft to failure. The turbofan engine is an important element and is the largest and most complex subsystem in an aircraft, so knowing the current status in real-time is important. They applied a fault prognosis convolutional neural network (FP-CNN) to the measured vibration response at NASA AMES Lab to predict the remaining useful life (RUL). Intelligent fault diagnosis techniques improve the efficiency of fault diagnosis by replacing time-consuming and unreliable human interpretation. Deep learning has been applied to improve the accuracy of intelligent fault diagnosis. [PERSON] et al. [129] applied deep learning to the vibration response generated by the cutting of an end mill. The vibration generated during aluminum processing with a three-tooth end mill cutter attached to the spindle housing was measured. The specific frequency of the chatter generated during the process was measured. This corresponded to the natural frequency measured by the impact test. [PERSON] et al. used the Mel-frequency cepstral coefficient (MFCC) and wavelet as well as the raw data for input features. MFCC uses the concept of the cepstrum, which is an effective method for signal processing the vibrations from rotating components in a milling machine. They obtained a much better performance compared with the deep learning error with k-means and DBN. Data-driven methods have been used for health monitoring by sensing and data analysis. Noise and the irregular length of data or sampling make classification with a regression model difficult. [PERSON] et al. [130] proposed a convolutional bidirectional long short-term memory (CBLSTM) network that features both a CNN and long short-term memory (LSTM). Local information is extracted by the CNN. Then, time series data are encoded by LSTM. The bidirectional LSTM extracts long-term dependencies from the time series data and uses bidirectionality to take advantage of the past and future contexts. Figure 7: Diagnosis result of the four tests from [108], Copyright 2019, MDPI. They predicted actual tool wear with raw data and compared the performance of CBLSTM with other types of discrimination. [PERSON] et al. applied the proposed method to computer numerical control (CNC) dry milling and acquired data by measuring vibration responses in different directions. The bogie is an important component in high-speed trains that directly affects the safety and comfort of the train operation. Depending on the condition of the bogie, large vibrations may occur in the train, which can lead to accidents such as derailment or overturning. [PERSON] et al. [131] suggested applying deep learning to the existing method for diagnosing bogie faults. The advantages of deep learning by comparison with the firefly artificial neural network (FANN), particle swarm optimization neural network (PSONN) and genetic artificial neural network (GANN). They applied dynamic modeling to high-speed trains and diagnosed eight states of bogie vibrations. Collecting actual vibrations from a train with a failed bogie system is very difficult and dangerous. Therefore, the multibody dynamics software SIMPACK was used to simulate high-speed trains. Many sensors were mounted on the bogie, and measurements were carried out for diagnosis. So far, examples of deep learning for diagnosing the vibration of a rotating object have been introduced. Gears and bearings were the main focus of study, and breakage was the main cause. The specific frequency component that occurs at this time does not overlap with the rotation frequency, so the failure occurrence can be easily determined.