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LIU Jin-chao, LI Guo-qing, SUN Shan-chao, ZHAO Gang, GUO Jian-feng. Improved fuzzy method of removing abnormal spike data from track's geometric irregularity of high-speed railway[J]. Journal of Traffic and Transportation Engineering, 2016, 16(2): 37-45. doi: 10.19818/j.cnki.1671-1637.2016.02.005
Citation: LIU Jin-chao, LI Guo-qing, SUN Shan-chao, ZHAO Gang, GUO Jian-feng. Improved fuzzy method of removing abnormal spike data from track's geometric irregularity of high-speed railway[J]. Journal of Traffic and Transportation Engineering, 2016, 16(2): 37-45. doi: 10.19818/j.cnki.1671-1637.2016.02.005
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Improved fuzzy method of removing abnormal spike data from track's geometric irregularity of high-speed railway

doi: 10.19818/j.cnki.1671-1637.2016.02.005

  • Infrastructure Inspection Institute, China Academy of Railway Sciences, Beijing 100081, China

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  • Author Bio:

    LIU Jin-zhao(1971-), male, researcher, PhD, +86-10-51893555, liujinzhao_2010@sina.com

  • Received Date: 2015-11-13
  • Publish Date: 2016-04-25
    Abstract
  • According to the mutation property of abnormal spike data for track's geometric irregularity of high-speed railway, an improved fuzzy method of removing abnormal spike data was designed.The point with unexpectedly increasing or decreasing forward difference was defined as the starting point of abnormal spike data, the following point being close to the starting point and with reversely changing forward difference was extracted as the end point of abnormal spike data, and the abnormal spike data were removed and replaced by the linear interpolation between the two endpoints of the minimum interval including the abnormal spike data.The membership functions were constructed by using the unit decomposition principle, and apre-judgment was proposed to avoid calculating the forward differences, the functions of firing degree of rules and the fuzzy basis functions when the track geometry data were normal.Calculation result shows that the improved method has the same accuracy as the previously used fuzzy filter method, but its computation time is less than 0.33% of the original one when the computation distance is longer than 500 km, so it is more easily achieved online.The errors between the analog signals and the measured track geometry irregularities are less than 10-3 when the improved method is used for detecting and removing the abnormal spike data.The improved method is able to not only accurately identify the locations of abnormal spike data andautomatically restore the signals, but also retain useful information such as the large alignments and gauges around the switches.

     

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