A recursive Bayesian approach to small fatigue crack propagation and detection modeling. (December 2018)

• Record Type: Journal Article
• Authors: Smith, Reuel; Modarres, Mohammad; Droguett, Enrique López
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 232:Number 6(2018:Dec.)
• Page Start: 738
• Page End: 753
• Publication Date: 2018-12
• Subjects: Small crack propagation -- probability of detection -- recursive Bayesian estimation -- acoustic emission -- joint particle filtering -- Gaussian process regression; Reliability (Engineering) -- Mathematical models -- Periodiclals; Risk assessment -- Mathematical models -- Periodicals; Engineering design -- Mathematical models -- Periodicals; 620.00452
• DOI: 10.1177/1748006X18758721
• Languages: English
• ISSNs: 1748-006X

Abstract

Engineers have witnessed much advancement in the study of fatigue crack detection and propagation modeling. More recently, the use of certain damage precursors such as acoustic emission signals to assess the integrity of structures has been proposed for application to prognosis and health management of structures. However, due to uncertainties associated with small crack detection of damage precursors and crack size measurement errors of the detection technology used, applications of prognosis and health management assessments have been limited. In this article, a methodology is developed for the purpose of assessment of crack detection and propagation parameters and the minimization of uncertainties including detection and sizing errors associated with a series of known crack detection and propagation models that use acoustic emission as the precursor to fatigue cracking. The methodology is facilitated by the Bayesian inference of a joint-likelihood model which includes sizing and detection models. Examples where several dog-bone Al 7075T6 specimens are tested to produce fatigue crack initiation and propagation data and estimates based on remaining useful life support the effectiveness and usefulness of the proposed methodology.