Digital Damage Fingerprints (DDF) and its application in quantitative damage identification

Zhongqing Su, Lin Ye

Research output: Journal article publicationJournal articleAcademic researchpeer-review

20 Citations (Scopus)


One challenge for structural damage identification using active sensor network is how to appropriately define and extract feature components from raw signals, so as to faithfully describe the damage to be identified. Motivated by this, a signal processing and interpretation technique based on a novel concept, Digital Damage Fingerprints (DDF), was developed in this study, particularly for the purpose of quantitative identification of structural damage. Such an approach is able to efficiently identify and digitise characteristics in signals acquired from active sensor network, and consequently quantify a complicated structure using concise yet essential information. For validation, the technique was then applied to the development of Damage Parameters Databases (DPDs) and online quantitative identification of through-hole and delamination damage in CF/EP (T650/F584) composite structures, under assistance of an artificial neural algorithm. The results exhibit excellent performance of DDF technique in system pattern recognition.
Original languageEnglish
Pages (from-to)197-204
Number of pages8
JournalComposite Structures
Issue number2 SPEC. ISS.
Publication statusPublished - 1 Feb 2005
Externally publishedYes


  • Artificial neural network
  • Composite structures
  • Damage detection
  • Pattern recognition
  • Signal processing
  • Wavelet transform

ASJC Scopus subject areas

  • Ceramics and Composites
  • Civil and Structural Engineering


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