Research on prediction method of life cycle of track quality based on grey system identification

被引：0
作者：
机构：
[1] Infrastructure Inspection Institute, China Academy of Railway Sciences
来源：
Qu, J.-J. (qujianjun0801@126.com) | 1600年 / Science Press卷 / 34期
关键词：
Grey system identification; Life cycle; Prediction model; Tamp operation; Time series of TQI; Track quality;
D O I：
10.3969/j.issn.1001-8360.2012.09.013
中图分类号：
学科分类号：
摘要：
The grey nonlinear model put forward by the author (see the Journal of the China Railway Society, 2010, 2) is viewed as a time function controlled by a series of parameters including trend coefficients and random coefficients. The coefficients are identification parameters for system analysis, which represent the characteristics that the system is varying with time. The prediction model is established under the conditions of knowing tamp operating efficiency of large machine or initial quality of railway track. The relevance among track quality systems in different maintenance periods is assumed, and then TQI characteristics in the maintenance period required predicting can be obtained by data mining on identification parameters of TQI in the known maintenance periods. Through the calculation example we can see the prediction model proposed in this paper can predict the development of track quality in different maintenance periods accurately and also provides a new idea for the research on life cycle of track quality.
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页码：75 / 80
页数：5
相关论文
共 12 条
  • [1] (2003)
  • [2] Xu Y.-D., Li H.-F., Zhou Y., Forecast methods of track longitudinal level irregularity, Journal of Tongji University, 31, 3, pp. 291-295, (2003)
  • [3] Xu Y.-D., Wu J.-C., Analysis on development of track irregularities with linear forecast model, Journal of Shijiazhuang Railway Institute, 18, 3, pp. 6-9, (2005)
  • [4] Chen X.-M., Wang L., Yang F.-C., Et al., Integrating factor method for predicting the developing trend of railway track irregularity, China Railway Science, 27, 6, pp. 27-31, (2006)
  • [5] Wang J.-X., Li H.-F., Xu Y.-D., Evaluation and prediction method for railway track geometric state based on probability distribution change, China Railway Science, 29, 5, pp. 31-34, (2008)
  • [6] Qu J.-J., Gao L., Zhang X.-K., Et al., The application of modified unequal time-interval grey GM(1, 1) model in track quality's prediction, China Railway Science, 30, 3, pp. 5-8, (2009)
  • [7] Qu J.-J., Gao L., Tian X.-Y., Study on the mid long term prediction model of track geometry state basing on the grey time-varying parameters theory, Journal of the China Railway Society, 32, 2, pp. 55-59, (2010)
  • [8] (2006)
  • [9] Rivier R.E., Korpanec I., ECOTRACK: a tool to reduce the life cycle costs of the track, Proceedings of the World Congress on Railway Research, pp. 289-295, (1997)
  • [10] Jovanovic S., Esveld C., ECOTRACK: An objective condition-based decision support system for long-term track M&R planning directed towards reduction of life cycle costs, 7th International Heavy Haul Conference, (2001)
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