Fiber Optic Train Monitoring with Distributed Acoustic Sensing: Conventional and Neural Network Data Analysis Distributed acoustic sensing (DAS) over tens of kilometers of fiber optic cables is wellsuited for monitoring extended railway infrastructures. As DAS produces large, noisy datasets, it is important to optimize algorithms for precise tracking of train position, speed, and the number of train cars. The purpose of this study is to compare different data analysis strategies and the resulting parameter uncertainties. We present data of an ICE 4 train of the Deutsche Bahn AG, which was recorded with a commercial DAS system. We localize the train signal in the data either along the temporal or spatial direction, and a similar velocity standard deviation of less than 5 kmh for a train moving at 160 kmh is found for both analysis methods. The data can be further enhanced by peak finding as well as faster and more flexible neural network algorithms. Then, individual noise peaks due to bogie clusters become visible and individual train cars can be counted. From the time between bogie signals, the velocity can also be determined with a lower standard deviation of 0.8 kmh. The analysis methods presented here will help to establish routines for near realtime train tracking and train integrity analysis.

Using a Machine Learning Algorithm Integrated with Data DeNoising Techniques to Optimize the Multipoint Sensor Network In this paper, for an intensity wavelength division multiplexing (IWDM)based multipoint fiber Bragg grating (FBG) sensor network, an effective strain sensing signal measurement method, called a long shortterm memory (LSTM) machine learning algorithm, integrated with data denoising techniques is proposed. These are considered extremely accurate for the prediction of very complex problems. Four ports of an optical coupler with distinct output power ratios of 70%, 60%, 40%, and 30% have been used in the proposed distributed IWDMbased FBG sensor network to connect a number of FBG sensors for strain sensing. In an IWDMbased FBG sensor network, distinct power ratios of coupler ports can contain distinct powers or intensities. However, unstable output power in the sensor system due to random noise, harsh environments, aging of the equipment, or other environmental factors can introduce fluctuations and noise to the spectra of the FBGs, which makes it hard to distinguish the sensing signals of FBGs from the noise signals. As a result, noise reduction and signal processing methods play a significant role in enhancing the capability of strain sensing. Thus, to reduce the noise, to improve the signaltonoise ratio, and to accurately measure the sensing signal of FBGs, we proposed a long shortterm memory (LSTM) deep learning algorithm integrated with discrete waveform transform (DWT) data smoother (denoising) techniques. The DWT data denoising methods are important techniques for analyzing and denoising the sensor signals, and it further improves the strain sensing signal measurement accuracy of the LSTM model. Thus, after denoising the sensor data, these data are fed into the LSTM model to measure the sensing signal of each FBG. The experimental results prove that the integration of LSTM with the DWT data denoising technique achieved better sensing signal measurement accuracy, even in noisy data or environments. Therefore, the proposed IWDMbased FBG sensor network can accurately sense the signal of strain, even in bad or noisy environments; can increase the number of FBG sensors multiplexed in the sensor system; and can enhance the capacity of the sensor system.