Volume 18 Issue 6
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2018  >  18(6): 101-111
XIE Guo, ZHANG Yong-yan, SHANGGUAN An-qi, DU Xu-long, HEI Xin-hong, GAO Qiao-sheng, WANG Yue-kuan. Soft measurement method for temperature monitoring data of train axle[J]. Journal of Traffic and Transportation Engineering, 2018, 18(6): 101-111. doi: 10.19818/j.cnki.1671-1637.2018.06.011
Citation: XIE Guo, ZHANG Yong-yan, SHANGGUAN An-qi, DU Xu-long, HEI Xin-hong, GAO Qiao-sheng, WANG Yue-kuan. Soft measurement method for temperature monitoring data of train axle[J]. Journal of Traffic and Transportation Engineering, 2018, 18(6): 101-111. doi: 10.19818/j.cnki.1671-1637.2018.06.011
shu

Soft measurement method for temperature monitoring data of train axle

doi: 10.19818/j.cnki.1671-1637.2018.06.011
  • 1.

    Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi'an University of Technology, Xi'an 710048, Shaanxi, China

  • 2.

    Department of Computer Engineering, Nihon University, Funabashi 274-8501, Chiba, Japan

More Information
  • Author Bio:

    XIE Guo(1982-), male, professor, PhD, guoxie@xaut.edu.cn.

  • Received Date: 2018-06-11
  • Publish Date: 2018-12-25
    Abstract
  • To solve the high misdiagnosis and missing report rates of existing axle temperature monitoring system caused by data missing, a soft measurement method based on the characteristic analysis of axle temperature monitoring data was proposed.By analyzing the layout and correlation of axle temperature monitoring points, the source data range of soft measurement for monitoring data was determined.By using the self-organizing feature mapping algorithm, the intrinsic dimension of axle temperature data was determined.The data were clustered through source data normalization, winning region definition and membership degree optimization.By introducing the multi-dimensional scaling analysis method, the dimension reduction of axle temperature data in the internal class was realized through data space quantization and similarityas well as eigenvalue decomposition of the distance matrix. The multi-dimensional scaling analysis method was reused to reduce the dimension of data between external classes.A step-bystep dimension reduction method was proposed, and an equilibrium strategy was built to maximize the amount of information and minimize the calculations.The stacked auto encoder of a deep learning method was utilized to extract the internal characteristics of data between external classes, and a soft measurement model of axle temperature missing data was constructed.Research result shows that the time efficiency of the soft measurement method based on the dimension reduced data is 14.25% higher than that based on original data.The accuracies of the two methods maintain a similar level.When one-dimensional data is missed, the average accuracy of soft measurement data can reach 99.83%.When two-dimensional data is missed, the average accuracy can achieve 99.75%.When three-or four-dimensional data is missed, the average accuracy can arrive at 99.16%.Under the conditions that the maximum allowable error and error tolerance are 2.5% and 1.0%, respectively, as long as the dimension of missing data is not higher than four, the method can effectively recover the missing data with high accuracy and efficiency.

     

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