Abstract
Aiming at the safety and comfort of urban rail transit operation, a machine learning KNN algorithm is proposed to predict the degree of track irregularity. There are many fundamental factors that cause the track to be uneven, mainly reflected in the lateral irregularity of the track and the vertical irregularity. In this paper, the inertial navigation system is used to realize the acquisition of the orbital dynamic acceleration data, and the secondary displacement of the acceleration can obtain the relevant displacement information. The measurement of the gauge distance can be achieved by using a laser displacement sensor. By extracting the different displacement feature data of the same position at the same time and calculating the distance between the feature values, the purpose of predicting the degree of track damage can be achieved.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IOP Conference Series: Materials Science and Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
