4.7 Article

Impact Damage Detection and Identification Using Eddy Current Pulsed Thermography Through Integration of PCA and ICA

Journal

IEEE SENSORS JOURNAL
Volume 14, Issue 5, Pages 1655-1663

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2014.2301168

Keywords

Eddy current pulsed thermography; non-destructive evaluation; principal component analysis; independent component analysis; impact damage; spatial-temporal pattern separation

Funding

  1. National Natural Science Foundation of China [51377015]
  2. Sichuan Science and Technology Department [2013HH0059]
  3. University of Electronic Science and Technology of China
  4. National Research Center of Sensors Engineering
  5. Shenyang Academy of Instrumentation Company Ltd.
  6. Health Monitoring of Offshore Wind Farms
  7. Cognitive-Networks-Enabled Transnational Proactive Healthcare
  8. Engineering and Physical Sciences Research Council (EPSRC), U. K. [EP/F06151X/1]
  9. FP7 Health Monitoring of Offshore Wind Farms (HEMOW) [FP7-PEOPLE-2010-IRSES-269202]
  10. Engineering and Physical Sciences Research Council [EP/F06151X/1] Funding Source: researchfish
  11. EPSRC [EP/F06151X/1] Funding Source: UKRI

Ask authors/readers for more resources

Eddy current pulsed thermography (ECPT) is implemented for detection and separation of impact damage and resulting damages in carbon fiber reinforced plastic (CFRP) samples. Complexity and nonhomogeneity of fiber texture as well as multiple defects limit detection identification and characterization from transient images of the ECPT. In this paper, an integration of principal component analysis (PCA) and independent component analysis (ICA) on transient thermal videos has been proposed. This method enables spatial and temporal patterns to be extracted according to the transient response behavior without any training knowledge. In the first step, using the PCA, the data is transformed to orthogonal principal component subspace and the dimension is reduced. Multichannel morphological component analysis, as an ICA method, is then implemented to deal with the sparse and independence property for detecting and separating the influences of different layers, defects, and their combination information in the CFRP. Because different transient behaviors exist, multiple types of defects can be identified and separated by calculating the cross-correlation of the estimated mixing vectors between impact the ECPT sequences and nondefect ECPT sequences.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available