High-speed Rail Comprehensive Detection Train Detection Data Abnormal Point Determination And Track Irregularity Degradation Trend Prediction

With the implementation of the "transportation powerhouse" strategy,railways,as the main artery of China's economic operation,will become an important tool to promote the implementation of the "transportation powerhouse" strategy.Ensuring the safety of railway operation is an important prerequisite for the normal operation of railway transportation and is also the core of the work of relevant railway departments.Taking the track irregularity detection data as the research object,mining the law of track condition degradation will help the railway related departments to scientifically and reasonably plan the track maintenance and repair plan,thereby ensuring the safety of train operation.On the basis of analyzing the domestic and foreign research on track quality degradation trend and the theory of track geometric irregularity prediction,this paper studies three aspects of track detection data abnormal value processing,track quality degradation characteristics and track geometric irregularity trend prediction.First,research on the causes and effects of the quality problems in the high-speed railway comprehensive detection train detection data,and distinguish the maintenance operation points from the abnormal values,and repair the abnormal values after screening.Secondly,analyze the processed inspection data and explore the law of track quality state change.Finally,it is concluded that there are three obvious characteristics of track quality state change,namely,waveform change,periodicity,and degradation rate.Subsequently,according to the characteristics of the deterioration of the track quality state,the trend prediction model of the track geometric irregularity was improved and constructed on the basis of the linear regression prediction model.In the end,the trend of track quality status changes is predicted,combined with track quality maintenance management standards,to guide the preparation of maintenance and repair plans.This paper collects the track detection data of the detected trains at different mileages on the Changfu Line from November 2014 to April 2019,and uses the above data to verify the abnormal data processing method proposed in this paper.The results show that the method can effectively identify and process abnormal data.At the same time,the processed data is used to verify and analyze the accuracy of the orbit geometric irregularity trend prediction model.The results show that the effect of the orbit geometric irregularity trend prediction model proposed in this paper is good.