Study on Intelligent Diagnosis of Railway Turnout Switch Based on Improved FastDTW and Time Series Segmentation under Big Data Monitoring

Abstract

Turnout equipment is a key component to ensure the safe operation of trains. How to identify turnout faults is one of the important tasks of railway engineering departments and electrical departments. We used machine learning algorithm to analyze the similarity of mechanical characteristic data during turnout actions and then realized the intelligent diagnosis of turnout faults under the background of big data. We segmented the mechanical motion curve according to the characteristics of the original curve, calculated the similarity between the motion curve to be diagnosed and the template motion curve through the improved fast dynamic time warping algorithm (FastDTW), and diagnosed the fault according to the dynamically determined optimal threshold. Experiments show that the algorithm for predicting turnout faults based on improved FastDTW and time series segmentation algorithm is an accurate and effective method, which can improve the key technology of intelligent monitoring, early warning of the whole process of turnout movement, and more detailed analysis of the motion curve for the railway department.

Wireless sensing in high-speed railway turnouts with battery-free materials and devices

“…From the standpoint of a machine learning algorithm, to accomplish intelligent detection of turnout defects against the backdrop of big data, 22 an intelligent diagnostic technique based on deep learning curve segmentation and the Support Vector Machine was provided, and the suggested method’s diagnostic accuracy can reach 98.5%. 23 In addition, an intelligent diagnosis approach for railway turnouts using Dynamic Time Warping was created to analyze five different types of turnout faults.…”