Acousto-ultrasonics-based fatigue damage characterization: Linear versus nonlinear signal features

Zhongqing Su, Chao Zhou, Ming Hong, Li Cheng, Qiang Wang, Xinlin Qing

Research output: Journal article publicationJournal articleAcademic researchpeer-review

120 Citations (Scopus)

Abstract

Engineering structures are prone to fatigue damage over service lifespan, entailing early detection and continuous monitoring of the fatigue damage from its initiation through growth. A hybrid approach for characterizing fatigue damage was developed, using two genres of damage indices constructed based on the linear and the nonlinear features of acousto-ultrasonic waves. The feasibility, precision and practicability of using linear and nonlinear signal features, for quantitatively evaluating multiple barely visible fatigue cracks in a metallic structure, was compared. Miniaturized piezoelectric elements were networked to actively generate and acquire acousto-ultrasonic waves. The active sensing, in conjunction with a diagnostic imaging algorithm, enabled quantitative evaluation of fatigue damage and facilitated embeddable health monitoring. Results unveiled that the nonlinear features of acousto-ultrasonic waves outperform their linear counterparts in terms of the detectability. Despite the deficiency in perceiving small-scale damage and the possibility of conveying false alarms, linear features show advantages in noise tolerance and therefore superior practicability. The comparison has consequently motivated an amalgamation of linear and nonlinear features of acousto-ultrasonic waves, targeting the prediction of multi-scale damage ranging from microscopic fatigue cracks to macroscopic gross damage.
Original languageEnglish
Pages (from-to)225-239
Number of pages15
JournalMechanical Systems and Signal Processing
Volume45
Issue number1
DOIs
Publication statusPublished - 3 Mar 2014

Keywords

  • Acousto-ultrasonics
  • Fatigue damage characterization
  • Linear signal features
  • Nonlinear signal features
  • Piezoelectric sensor network
  • Structural health monitoring

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Civil and Structural Engineering
  • Aerospace Engineering
  • Mechanical Engineering
  • Computer Science Applications

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