Research On The Data Quality Control Algorithm Of The Track Alignment And Height Of Track Checker

Although inspection technology of track checker has been developed in China for more than ten years,relatively few research results have been reported in precise adjustment of long-tracks.In the use of track checker for the rapid inspection of ballast and ballastless track,the attached foreign matters(rock ballast,oil,cables,etc.)of track surface and inner side result in the lateral and vertical deviation mutation of the inspection track of track checker.Because measurement site is far,and network signal is weak,measurement data can't be transmitted to indoor work promptly,resulting in the mutation position can't be found on measurement site.It could consume a lot of manpower and resources.Therefore,it is necessary to conduct deeply research on the question that how to control the quality of the track inspection data of track checker.In order to realize the quality control of inspection data,this paper explores the mutation position of track alignment and height from the point of repeated measurement,and then establishes the basis for the mutation judgment.First of all,for the situation of segmented measurements,it is proposed to use the correlation coefficient method to merge the segmentation measurement data,so as to facilitate subsequent mutation detection of track alignment and height and internal data processing.After completing data preprocess,dividing the quality control of inspection data into two steps.Firstly,detecting the mutation position of track alignment and height.Due to the influence of the mileage measurement error,the systematic error is included in the round trip measurement difference,and the system error is uncertain and difficult to separate.The mutation position cannot be accurately positioned by repeated measurement difference directly.In order to effectively separate the systematic errors and accurately locate the mutation position of track alignment and height,it is proposed to use the peak difference to detect the mutation.For the purpose of realizing the peak point matching,turning the point matching into line matching.And dividing the matching process into feature points extraction and matching.Second,counting the peek difference about track alignment and height of the round trip measurements.In order to set threshold for detection mutation counting the difference and mean value of eliminating systematic error,and regarding mutation as gross error to detect mutation position of the lateral and horizontal deviation by using gross error detection algorithm.Finally,comparing and analyzing inspection data quality under various measurement conditions to verify further correctness and applicability of the matching algorithm.Measured data indicates that the correlation coefficient method is used to merge the segmented data,which can smoothly connect the segmented data.The peek point matching algorithm can accurately match the peak point of the round trip measurement and detect the measured waveform difference of round trip,effectively eliminating the system error caused by the mileage measurement error.The algorithm is practical,and easy to program and understand.Using the statistical analysis method to determine the mutation threshold of track alignment and height,they are 0.7mm and 0.8mm respectively,which can meet the actual requirements.It is no significant difference in the inspection data quality of the track checker on the operated railway,newly ballastless track,and newly ballast track.And the matching algorithm is suitable for the difference detection of the round trip waveform acquired under different inspection conditions.