Intelligent feature selection techniques for pattern classification of Lamb wave signals

Hinders, Mark K.; Miller, Corey

doi:10.1063/1.4864833

Cited by 4 publications

(3 citation statements)

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“…Aldrin et al (2014) present ADR system for ultrasonic inspection of composites. Hinders and Miller (2014) show intelligent features selection and pattern classification in case of aluminum plates UT evaluation. Kesharaju and Nagarajah (2014) present machine learning system with principal component analysis (PCA)-based reduction of dimensionality applied for ceramic materials ultrasonic inspection.…”

Section: Introductionmentioning

confidence: 99%

Automatic defect recognition for pulsed terahertz inspection of basalt fiber reinforced composites

Łopato

2016

COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering

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Purpose – The purpose of this paper is to present a system for automatic recognition of defects detected in non-conductive polymer composites using pulsed terahertz imaging. Design/methodology/approach – On the beginning, non-destructive evaluation of composites using electromagnetic waves in terahertz frequency is shortly introduced. Next automatic defects recognition (ADR) algorithm is proposed, focussing on new features calculation. Dimensionality of features space is reduced by using principal component analysis. Finally, results of basalt fiber reinforced composite materials inspection and identification using artificial neural networks is presented and discussed. Findings – It is possible to develop ADR system for non-destructive evaluation of dielectric materials using pulsed terahertz technique. New set of features in time and frequency domains is proposed and verified. Originality/value – ADR in non-destructive testing is utilized in case of digital radiography and ultrasonic testing. Terahertz inspection with pulsed excitation is reported as a source of many useful information about the internal structure of the dielectric material. Up to now ADR based on terahertz non-destructive evaluation systems was not utilized.

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Section: Introductionmentioning

confidence: 99%

Automatic defect recognition for pulsed terahertz inspection of basalt fiber reinforced composites

Łopato

2016

COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering

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“…Some scholars have applied the time-frequency method to recognize different LW modes, but the results show varying degrees of limitations. The wavelet transform (WT) is used in Hinders' work to extract LW signal characteristics interacting with defects, and these statics are then fed to statistical pattern classification algorithms that identify flaw severity [5]. Researchers at the University of Sydney use WT and artificial neural network algorithms for mode recognition in CF/EP composite structures [6].…”

Section: Introductionmentioning

confidence: 99%

Mode Recognition of Lamb Wave Detecting Signals in Metal Plate Using the Hilbert-Huang Transform Method

Wang¹,

Huang²,

Zhao³

2015

JST

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The dispersion and multiple modes characteristics which exist in the propagation of Lamb waves (LW) in metal plates make it extremely hard to analyze and recognize the detection echo signals of defects. As a newly developed time-frequency analysis method in recent years, Hilbert-Huang transform (HHT) is one of the powerful tools to analyze non-stationary signals. The experimental LW detecting system for single aluminum plate is setup in this work, and the LW detecting signals are analyzed by HHT. The overlapped LW detecting signals of different modes are recognized by the means of extracting flight time of intrinsic mode functions (IMFs) after Hilbert transform (HT). The experiment results, agreeing well with the theoretical analysis, indicate that the HHT method can clearly recognize overlapped LW detecting signals of different modes in metal plates, but could hardly recognize that of the same mode. HHT can be an effective method to recognize LW detecting signals of different modes in metal plates.

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“…Hindersetal. [5]extracted cracks features from the Lamb wave signals using wavelets and statistical pattern classification algorithms to identify flaw severity.…”

Section: Introductionmentioning

confidence: 99%

Rapid Defects Features Extract Techniques with Chirp Signal in Plate Structures

Deng¹,

Chen²

2015

Proceedings of the 2015 International Conference on Modeling, Simulation and Applied Mathematics

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Abstract-This work presents a convenience means to get defects features with linear chirp signal and demonstrate it with finite element method and BP neural networks. Aluminum plate models with various kinds of defects modeling with numerical simulation software ABAQUS to get pattern recognition samples.In order to acquire tone burst responses at a wide range of frequencies, we take chirp signals as excitation. The defects scattering signals, which would be used to build sample library, are extracted from the calculated tone burst results. .Finally, two 2-layers BP neural networks are utilized to realize defects types and sizes identification. Test results show that this rapid defects features extract approach is useful to recognize the defect characteristics, although some misidentification exist.

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Intelligent feature selection techniques for pattern classification of Lamb wave signals

Cited by 4 publications

References 0 publications

Automatic defect recognition for pulsed terahertz inspection of basalt fiber reinforced composites

Automatic defect recognition for pulsed terahertz inspection of basalt fiber reinforced composites

Mode Recognition of Lamb Wave Detecting Signals in Metal Plate Using the Hilbert-Huang Transform Method

Rapid Defects Features Extract Techniques with Chirp Signal in Plate Structures

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