A multi-feature integration method for fatigue crack detection and crack length estimation in riveted lap joints using Lamb waves

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

Research output: Contribution to journalArticlepeer-review

42 Scopus citations

Abstract

This paper presents an experimental study of damage detection and quantification in riveted lap joints. Embedded lead zirconate titanate piezoelectric (PZT) ceramic wafer-type sensors are employed to perform in situ non-destructive evaluation (NDE) during fatigue cyclical loading. PZT wafers are used to monitor the wave reflection from the boundaries of the fatigue crack at the edge of bolt joints. The group velocity of the guided wave is calculated to select a proper time window in which the received signal contains the damage information. It is found that the fatigue crack lengths are correlated with three main features of the signal, i.e., correlation coefficient, amplitude change, and phase change. It was also observed that a single feature cannot be used to quantify the damage among different specimens since a considerable variability was observed in the response from different specimens. A multi-feature integration method based on a second-order multivariate regression analysis is proposed for the prediction of fatigue crack lengths using sensor measurements. The model parameters are obtained using training datasets from five specimens. The effectiveness of the proposed methodology is demonstrated using several lap joint specimens from different manufactures and under different loading conditions.

Original languageEnglish (US)
Article number105007
JournalSmart Materials and Structures
Volume22
Issue number10
DOIs
StatePublished - Oct 1 2013

ASJC Scopus subject areas

  • Signal Processing
  • Civil and Structural Engineering
  • Atomic and Molecular Physics, and Optics
  • Materials Science(all)
  • Condensed Matter Physics
  • Mechanics of Materials
  • Electrical and Electronic Engineering

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