4.8 Article

Induction Infrared Thermography and Thermal-Wave-Radar Analysis for Imaging Inspection and Diagnosis of Blade Composites

期刊

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 14, 期 12, 页码 5637-5647

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2018.2834462

关键词

Blade; diagnosis; imaging inspection; induction infrared thermography; machine vision; non-destructive infrared imaging; on-condition maintenance; thermographic analysis; Thermal-Wave Radar NDI; wind turbine

资金

  1. National Key Research and Development Program of China [2016YFF0203400]
  2. National Natural Science Foundation of China [61501483, 51408071]
  3. China Postdoctoral Science Foundation [2017M612549, 2017T100598]
  4. Natural Sciences and Engineering Research Council of Canada (NSERC)
  5. NSERC

向作者/读者索取更多资源

Condition monitoring, nondestructive testing, and fault diagnosis are currently considered crucial processes for on-condition maintenance (OCM) to increase the reliability and availability of wind turbines and reduce the wind energy generation cost. Carbon fiber reinforced plastics (CFRPs) have been increasingly used to fabricate wind turbine blades. Delamination-type damage is inevitable during manufacture or in-service of a CFRP blade. This inner (subsurface) flaw, usually difficult to be detected by artificial visual inspection or machine vision based on CCD or CMOS, severely degrades the load-bearing capacity of a blade. Induction infrared thermography (IIT) is an emerging infrared machine vision inspection technology, which has the capability of insight to CFRP based on electromagnetic induction and heat conduction. This paper introduces photothermal thermal-wave radar (TWR) nondestructive imaging (NDI) to IIT, based on cross-correlation (CC) pulse compression and matched filtering and applies TWR principles to CFRP imaging inspection and diagnosis. The experimental studies carried out under the transmission mode have shown that TWR B-scan and phasegram can be used to inspect and diagnose subsurface delaminations in CFRP with improved signal-to-noise ratio (SNR) and shape identification. As a new machine vision inspection method, TWRI will play an important role in the OCM of the wind turbine blade.

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