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-  2016 

高速铁路轨道几何不平顺改进模糊消刺方法
Improved fuzzy method of removing abnormal spike data from track’s geometric irregularity of high-speed railway

Keywords: 铁道工程,轨道几何不平顺,毛刺异常值,模糊滤波方法,单位分解原理,有序中值滤波方法
railway engineering
,track geometry irregularity,abnormal spike data,fuzzy filter method,unit decomposition principle,ranked order mean filter method

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Abstract:

根据高速铁路轨道几何不平顺毛刺异常值突然变化的特性,设计了改进模糊消刺方法,将差分突然增大或变小的点定义为毛刺异常值的起点,将与起点相近的差分反向突然变化的点定义为毛刺异常值的终点,选取包含毛刺异常值的最小区间的两个端点为插值点,用其近似线性插值代替原来的毛刺异常值。按照单位分解原理构造隶属度函数,并通过增加预判断,避免数据点正常时计算向前差分、规则激发度量函数和模糊基函数。计算结果表明:改进方法的计算精度与原模糊滤波方法相同,但当计算长度大于500 km时,计算时间小于原来的1/300,它更适合在线实现; 利用改进方法对包含毛刺异常值的模拟轨道几何不平顺信号进行消刺处理,滤波后的信号与解析信号的误差小于10-3; 利用改进方法不但能准确识别毛刺异常值的位置并自动修复,而且能完整保留道岔处大轨距和大轨向等有用信息。
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 a pre-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 and automatically restore the signals, but also retain useful information such as the large alignments and gauges around the switches. 1 tab, 26 figs, 25 refs

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