Tracking and ensuring the safety of trains is an important issue in subway operation management. Under the long-distance monitoring requirements, extracting features in real-time from large-scale stream data to track trains is a large and time-consuming task. With the support of the dynamic and distributed monitoring capabilities of ultra-weak fiber Bragg grating (UWFBG) arrays, this paper proposes a method combining the singular value decomposition (SVD) and the sequential similarity detection algorithm (SSDA) to handle the stream data to track trains in real-time. First, the vibration signal is denoised and is converted into a grayscale image using sliding window. Then, to improve the efficiency of recognition, the singular value features and the texture features are combined to build a template library for gray-scale image matching on the basis of SVD and SSDA. The details of SVD-SSDA deployment on Spark are illustrated to ensure real-time performance. Finally, the experimental results on the actual train data indicate that SVD-SSDA on Spark using ultra-weak FBG arrays can effectively identify the data stream and satisfy the requirements for real-time train tracking.
Monitoring the train vibration is an important issue in subway safety management and maintenance. Aiming at the problems of traditional technology for detection and acquisition of subway vibration, such as unable to achieve dynamic detection or evaluation, and unable to give early warning to the changes of subway tunnel structure, this paper proposes a method to obtain and predict the vibration reduction effect based on grating array. The method uses short-time power spectral density (PSD) to extract train vibration signal and uses Z-vibration level (VLz) to obtain the vibration reduction effect of subway track. A model based on deep forest (DF) is improved to predict the variation trend of vibration reduction effect. The experimental results on the actual train data illustrate that the proposed method can accurately extract the train vibration signal, and the model can effectively forecast the vibration reduction effect, which has a lower error precision and shows improved performance over other prediction models.
High-speed railway has achieved remarkable development in China, and safety monitoring of high-speed railway is becoming an important research. Fiber bragg grating (FBG) sensing technology is applied for monitoring and early warning system of high-speed railway track condition in this paper. The sensor network is built by putting FBG sensors on the high-speed rail tracks, which is necessary for real-time online monitoring of railway track temperature, displacement and strain. These different variables are collected, processed and analyzed by FBG demodulator. In addition, the railway track temperature prediction model are established based on relevance vector regression algorithm, which further improves the prediction accuracy and generalization performance. The system has been applied in the realtime online monitoring and early warning system of Guangzhou-Shenzhen-Hong Kong high-speed railway track condition. The system is running in good condition and playing an important role in early warning.
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