Analysis of the accuracy of a neural algorithm for defect depth estimation using PCA processing from active thermography data

Dudzik, Sebastian

doi:10.1016/j.infrared.2012.08.006

Cited by 21 publications

(9 citation statements)

References 11 publications

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“…In particular, the algorithm of dynamic thermal tomography is implemented with the use of these methods [4]. Another approach is based on a comprehensive statistical analysis of the entire recorded sequence of thermograms, which uses the principal components analysis method [5]. Each of these methods has its advantages and disadvantages, but they are all used to solve a narrow range of tasks and are not universal and adaptive [6].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Automated System for Thermal Defectometry of Multilayer Materials

Momot

Galagan

Глуховский

2021

Prib. metody izmer.

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Currently, along with growth in industrial production, the requirements for product quality testing are also increasing. In the tasks of defectoscopy and defectometry of multilayer materials, the use of thermal nondestructive testing method is promising. At the same time, interpretation of thermal testing data is complicated by a number of factors, which makes the use of traditional methods of data processing ineffective. Therefore, an urgent task is to search for new methods of thermal testing that will automate the diagnostic process and increase information content of obtained results. The purpose of article is to use the advances in deep learning for processing results of active thermal testing of products made of multilayer materials and development of an automated system for thermal defectoscopy and defectometry of such products. The proposed system consists of a heating source, an infrared camera for recording sequences of thermograms and a digital information processing unit. Three neural network modules are used for automated data processing, each of which performs one of the tasks: defects detection and classification, determination of the defect depth and thickness. The software algorithms and user interface for interacting with system are programmed in the NI LabVIEW development environment.Experimental studies on samples made of multilayer fiberglass have shown a significant advantage of the developed system over using traditional methods for analyzing thermal testing data. The defect classification (determining the type) error on the test dataset was 15.7 %. Developed system ensured determination of defect depth with a relative error of 3.2 %, as well as the defect thickness with a relative error of 3.5 %.

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Section: Introductionmentioning

confidence: 99%

Deep Learning Automated System for Thermal Defectometry of Multilayer Materials

Momot

Galagan

Глуховский

2021

Prib. metody izmer.

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“…Within the machine learning (ML) approaches, regression learners study the relationship between one or more explanatory random variables and their responses [ 15 ]. Specifically, the artificial neural network (ANN) has been applied for regressions in various investigations with thermography to estimate the depth of the defects [ 4 , 16 ] or for biomedical applications [ 17 ]. ANN can be applied using visualization approaches that provide information about its behavior and structure [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Predictive Models for the Characterization of Internal Defects in Additive Materials from Active Thermography Sequences Supported by Machine Learning Methods

Rodríguez‐Martín

Fueyo

Aguilera

et al. 2020

Sensors

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The present article addresses a generation of predictive models that assesses the thickness and length of internal defects in additive manufacturing materials. These modes use data from the application of active transient thermography numerical simulation. In this manner, the raised procedure is an ad-hoc hybrid method that integrates finite element simulation and machine learning models using different predictive feature sets and characteristics (i.e., regression, Gaussian regression, support vector machines, multilayer perceptron, and random forest). The performance results for each model were statistically analyzed, evaluated, and compared in terms of predictive performance, processing time, and outlier sensibility to facilitate the choice of a predictive method to obtain the thickness and length of an internal defect from thermographic monitoring. The best model to predictdefect thickness with six thermal features was interaction linear regression. To make predictive models for defect length and thickness, the best model was Gaussian process regression. However, models such as support vector machines also had significative advantages in terms of processing time and adequate performance for certain feature sets. In this way, the results showed that the predictive capability of some types of algorithms could allow for the detection and measurement of internal defects in materials produced by additive manufacturing using active thermography as a non-destructive test.

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“…The success of these signal processing methods has one common ingredient that bridges the gap between the physical world and mathematical modeling world. For crack detection in the thermal field, several outstanding results based on advanced signal processing have been reported [22][23][24][25]. Vrana et.…”

Section: Introductionmentioning

confidence: 99%

“…al. researched the mechanisms and models for crack detection with induction thermography [22]. Dudzik proposed a neural algorithm to estimate defect depth using an active thermography [23]. Peng et.…”

Section: Introductionmentioning

confidence: 99%

Thermal Pattern Contrast Diagnostic of Microcracks With Induction Thermography for Aircraft Braking Components

Wang

Gao

Woo

et al. 2018

IEEE Trans. Ind. Inf.

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Reciprocating impact load leads to plastic deformation on the surface of the kinematic chains in aircraft brake system. As a result, this causes fatigue and various complex natural damages. Due to the complex surface conditions and the coexistence damages, it is extremely difficult to diagnose micro cracks by using conventional thermography inspection methods. In this paper, the Thermal Pattern Contrast (TPC) method is proposed for weak thermal signal detection using eddy current pulsed thermography (ECPT). In this process, the extraction and subsequent separation differentiate a maximum of the thermal spatial-transient pattern between defect and non-defect areas. Specifically, a successive optical flow (OF) is established to conduct a projection of the thermal diffusion. This directly gains the benefits of capturing the thermal propagation characteristics. It enables us to build the motion context connected between the local and the global thermal spatial pattern. Principal Component Analysis (PCA) is constructed to further mine the spatial-transient patterns to enhance the detectability and sensitivity in micro crack detection. Finally, experimental studies have been conducted on an artificial crack in a steel sample and on natural fatigue cracks in aircraft brake components in order to validate the proposed method.

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Analysis of the accuracy of a neural algorithm for defect depth estimation using PCA processing from active thermography data

Cited by 21 publications

References 11 publications

Deep Learning Automated System for Thermal Defectometry of Multilayer Materials

Deep Learning Automated System for Thermal Defectometry of Multilayer Materials

Predictive Models for the Characterization of Internal Defects in Additive Materials from Active Thermography Sequences Supported by Machine Learning Methods

Thermal Pattern Contrast Diagnostic of Microcracks With Induction Thermography for Aircraft Braking Components

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