The research on detection methods of GPS abnormal monitoring data based on control chart

被引:0
作者
Yi, Ting-Hua [1 ]
Guo, Qing [1 ]
Li, Hong-Nan [1 ]
机构
[1] School of Civil Engineering, Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian
来源
Gongcheng Lixue/Engineering Mechanics | 2013年 / 30卷 / 08期
关键词
Cumulative sum control chart; Global positioning system; Outlier detection; Shewhart control chart; Statistical process control;
D O I
10.6052/j.issn.1000-4750.2012.04.0280
中图分类号
学科分类号
摘要
In order to effectively detect GPS abnormal monitoring data, a mathematical model for the GPS observations outlier detection is established. A new method for outlier detection and early-warning of observations by controlling a chart in the statistical process is proposed. Since the GPS monitoring data are not normally distributed, transferring them to Q statistic by Kernel density estimation of cumulative distribution functions is raised, and based on this, the control chart of Q statistic used for GPS abnormal data detection is constructed. Finally, the detection capacity of a Shewhart control chart and a cumulative sum control chart on different abnormal offsets are compared and analyzed based on the simulation data. The results show that the two control charts offer certain advantages and complement each other. The Shewhart control chart is able to provide effective early-warning for the abnormal offsets 3 times of the standard deviations, but it is lacking in the detection capacity of small offsets; while the cumulative sum control chart can accurately detect the continuous small offsets even smaller as 0.5 times of the standard deviations, but the false alarm rate may be bigger with the increase of the offsets.
引用
收藏
页码:133 / 141
页数:8
相关论文
共 19 条
  • [1] Yi T., Li H., Structural Health Monitoring Based on GPS, pp. 24-25, (2009)
  • [2] Kaloop M.R., Li H., Sensitivity and analysis GPS signals based bridge damaged using GPS observations and wavelet transform, Measurement, 44, 5, pp. 927-937, (2011)
  • [3] Psimoulis P.A., Stiros S.C., A supervised learning computer-based algorithm to derive the amplitude of oscillations of structures using noisy GPS and robotic theodolites (RTS) records, Computers and Structures, 92, 0, pp. 337-348, (2012)
  • [4] Wong K.Y., Instrumentation and health monitoring of cable supported bridge, Structural Control and Health Monitoring, 11, 2, pp. 91-124, (2004)
  • [5] Kijewski-Correa T., Kochly M., Monitoring the wind-induced response of tall buildings: GPS performance and the issue of multipath effects, Journal of Wind Engineering and Industrial Aerodynamics, 95, 2, pp. 1176-1198, (2007)
  • [6] Li H.N., Yi T.H., Yi X.D., Et al., Measurement and analysis of wind-induced response of tall building based on GPS technology, Advances in Structural Engineering, an International Journal, 10, 1, pp. 83-93, (2007)
  • [7] Ni Y.Q., Xia Y., Liao W.Y., Et al., Technology innovation in developing the structural health monitoring system for Guangzhou New TV Tower, Structural Control and Health Monitoring, 16, 1, pp. 73-98, (2009)
  • [8] Yi T.H., Li H.N., Gu M., Recent research and applications of GPS based technology for bridge health monitoring, Science China: Technological Sciences, 53, 10, pp. 2597-2610, (2010)
  • [9] Zhou J., Mao S., Statistical Method in Quality Management, pp. 1-502, (2006)
  • [10] Wang Z., Cheng Y., Damage identification using time-domain response of structural under control and control charts, Engineering Mechanics, 26, 8, pp. 194-200, (2009)