Probabilistic fatigue damage prognosis of lap joint using Bayesian updating

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

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


A general framework for probabilistic prognosis and uncertainty management under fatigue cyclic loading is proposed in this article. First, the general idea using the Bayesian updating in prognosis is introduced. Several sources of uncertainties are discussed and included in the Bayesian updating framework. An equivalent stress level model is discussed for the mechanism-based fatigue crack growth analysis, which serves as the deterministic model for the lap joint fatigue life prognosis. Next, an in situ lap joint fatigue test with pre-installed piezoelectric sensors is designed and performed to collect experimental data. Signal processing techniques are used to extract damage features for crack length estimation. Following this, the proposed methodology is demonstrated using the experimental data under both constant and variable amplitude loadings. Finally, detailed discussion on validation metrics of the proposed prognosis algorithm is given. Several conclusions and future work are drawn based on the proposed study.

Original languageEnglish (US)
Pages (from-to)965-979
Number of pages15
JournalJournal of Intelligent Material Systems and Structures
Issue number8
StatePublished - May 10 2015


  • Bayesian updating
  • fatigue
  • lamb wave
  • prognosis
  • uncertainties

ASJC Scopus subject areas

  • General Materials Science
  • Mechanical Engineering


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