On-line updating Gaussian process measurement model for crack prognosis using the particle filter
作者: Jian ChenShenfang YuanHui Wang
作者单位: 1Research Center of Structural Health Monitoring and Prognosis, State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
刊名: Mechanical Systems and Signal Processing, 2020, Vol.140
来源数据库: Elsevier Journal
DOI: 10.1016/j.ymssp.2020.106646
关键词: Fatigue crack prognosisParticle filterMeasurement equationStructural health monitoringGaussian process modelGuided wave
原始语种摘要: Abstract(#br)The particle filter (PF) has shown great potential for on-line fatigue crack growth prognosis by combining crack measurements from structural health monitoring (SHM) techniques. In this method, a key problem is to construct the mapping between the feature extracted from SHM signals and the crack size. However, this mapping may be inaccurate since the data used for establishing the mapping is affected by uncertainties from sources like damage geometries, sensor placements, and boundary conditions. To deal with this problem, this paper proposes an on-line updating Gaussian process (GP) measurement model within the PF based crack prognosis framework. The GP measurement model outputs the mean and variance of the crack length corresponding to the feature of SHM signals, which are...
全文获取路径: Elsevier  (合作)
影响因子:1.913 (2012)

  • filter 过滤机
  • prognosis 预报
  • model 模型
  • process 过程
  • updating 更新
  • crack 裂隙
  • particle 颗粒
  • measurement 测量
  • monitoring 监视
  • equation 方程