Fatigue damage diagnosis and prognosis using bayesian updating

Tishun Peng, Jingjing He, Yongming Liu, Abhinav Saxena, Jose Celaya, Kai Goebel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, a methodology integrating a Lamb wave-based damage detection technique and a Bayesian updating method for remaining useful life (RUL) prediction is proposed. First, a piezoelectric sensor network is used to detect the fatigue crack size near the rivet holes in fuselage lap joints. Advanced signal processing and feature fusion is then used to quantitatively estimate the crack size. Second, a small time scale model is used as the physics model to predict the crack propagation for a given future loading and an estimate of initial crack length. Next, a Bayesian updating algorithm is implemented incorporating the damage diagnostic result and the small time scale model for RUL prediction. Probability distributions of model parameters are updated considering various uncertainties in the damage prognosis process. Finally, the proposed methodology is demonstrated using data from fatigue testing of realistic fuselage lap joints and the model predictions are validated using prognostics metrics.

Original languageEnglish (US)
Title of host publication54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference
StatePublished - Aug 15 2013
Event54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference - Boston, MA, United States
Duration: Apr 8 2013Apr 11 2013

Publication series

NameCollection of Technical Papers - AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference
ISSN (Print)0273-4508

Other

Other54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference
CountryUnited States
CityBoston, MA
Period4/8/134/11/13

ASJC Scopus subject areas

  • Architecture
  • Materials Science(all)
  • Aerospace Engineering
  • Mechanics of Materials
  • Mechanical Engineering

Fingerprint Dive into the research topics of 'Fatigue damage diagnosis and prognosis using bayesian updating'. Together they form a unique fingerprint.

  • Cite this

    Peng, T., He, J., Liu, Y., Saxena, A., Celaya, J., & Goebel, K. (2013). Fatigue damage diagnosis and prognosis using bayesian updating. In 54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference [AIAA 2013-1652] (Collection of Technical Papers - AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference).