Enhancing defects characterization in pulsed thermography by noise reduction. (March 2019)

• Record Type: Journal Article
• Authors: Marani, R.; Palumbo, D.; Galietti, U.; Stella, E.; D'Orazio, T.
• Is Part Of: NDT & E international. Volume 102(2019)
• Page Start: 226
• Page End: 233
• Publication Date: 2019-03
• Subjects: Pulsed thermography -- FIR filter -- Model approximation -- Decision forest; Nondestructive testing -- Periodicals; Contrôle non destructif -- Périodiques; Electronic journals; 620.1127
• DOI: 10.1016/j.ndteint.2018.12.009
• Languages: English
• ISSNs: 0963-8695

Abstract

Abstract: In the field of NDT techniques for aeronautic components of composite materials, the development of automatic and robust approaches for defect detection is largely desirable for both safety and economic reasons. This paper introduces a novel methodology for the automatic analysis of thermal signals resulting from the application of pulsed thermography. Input thermal decays are processed by a proper FIR filter designed to reduce the measurement noise, and then modeled to represent both sound regions and defective ones. Output signals are thus fitted on an exponential model, which approximates thermal contrasts with three robust parameters. These features feed a decision forest, trained to detect discontinuities and characterize their depths. Several experiments on actual sample laminates have proven the increase of the classification performance of the proposed approach with respect to related ones in terms of the reduction of missing predictions of defective classes. Highlights: The design of a specific FIR filter, able to reduce measurement noise in optical pulsed thermography. The enhancement of differences between thermal signals from pristine and defective regions for automatic characterization of defects within composite laminates. The parametric analysis of results obtained by changing the length of the input feature vectors. The selection of the best parameters to classify the defects accordingly to their depths. The comparison of results with the state-of-the-art of classification methods.