Depth detection of spar cap defects in large-scale wind turbine blades based on 3D heat conduction model using step heating infrared thermography

The defects dispersed in spar cap often lead to failure of large-scale wind turbine blades. To predict the residual service life of blade and make repair, it is necessary to detect the depth of spar cap defects. Step-heating thermography (SHT) is a common infrared technique in this domain. However, the existing methods of SHT on defect depth detection are generally based on 1D models, which are unable to accurately detect the depth of spar cap defects because of ignoring material anisotropy and in-plane heat flow. To improve the depth detection accuracy of spar cap defects, a 3D model based on the theory of heat transfer is established by using equivalent source method (ESM), and a defect depth criterion is proposed based on the analytical solution of heat conduction equation. The modeling process are as follows. The heat conduction model of SHT was established by ESM. Then, coordinate transformation, variables separation and Laplace transformation were utilized to solve the 3D heat conduction equation. A defect depth criterion was proposed based on emerging contrast Cr. A GFRP composites plate containing 12 square flat-bottom holes with different sizes and depths was manufactured to represent spar cap with large thermal resistance defects, such as Delamination and crack. Experimental results demonstrate the validity of 3D model. Then the model was applied to on-site SHT test of a 1.5 megawatts (MW) wind turbine blade. The test results prove that the depth detection accuracy of spar cap defects can be significantly improved by using 3D model. In addition, by using a improved principle component analysis (PCA) method containing contrast enhancement factor, artifacts can be reduced and the recognition time of defects can be shortened.

Building Façade Protection Using Spatial and Temporal Deep Learning Models Applied to Thermographic Data. Laboratory Tests

This paper proposes a methodology that combines spatial and temporal deep learning (DL) models applied to data acquired by InfraRed Thermography (IRT). The data were acquired from laboratory specimens that simulate building façades. The spatial DL model (Mask Region-Convolution Neural Network, Mask R-CNN) is used to identify and classify different artificial subsurface defects, whereas the temporal DL model (Gated Recurrent Unit, GRU) is utilized to estimate the depth of each defect, all in an autonomous and automated manner. An F-score average of 92.8 ± 5.4% regarding defect identification and classification, and a root-mean-square error equal to 1 mm in the estimation of defect depth equal to 10 mm as the best defect depth estimation, are obtained with this first application of a combination of spatial and temporal DL models to the IRT inspection of buildings.

Research on laser ultrasonic surface defect identification based on a support vector machine

To solve the problem of difficult quantitative identification of surface defect depth during laser ultrasonic inspection, a support vector machine-based method for quantitative identification of surface rectangular defect depth is proposed. Based on the thermal-elastic mechanism, the finite element model for laser ultrasound inspection of aluminum materials containing surface defects was developed by using the finite element software COMSOL. The interaction process between laser ultrasound and rectangular defects was simulated, and the reflected wave signals corresponding to defects of different depths under pulsed laser irradiation were obtained. Laser ultrasonic detection experiments were conducted for surface defects of different depths, and multiple sets of ultrasonic signal waveform were collected, and several feature vectors such as time-domain peak, center frequency peak, waveform factor and peak factor were extracted by using MATLAB, the quantitative defect depth identification model based on support vector machine was established. The experimental results show that the laser ultrasonic surface defect identification model based on support vector machine can achieve high accuracy prediction of defect depth, the regression coefficient of determination is kept above 0.95, and the average relative error between the true value and the predicted value is kept below 10%, and the prediction accuracy is better than that of the reflection echo method and BP neural network model.

Management of Grade II Furcation Defect in Mandibular Molars with Allograft and Alloplastic Bone Graft: A Clinico- Radiographic Study

Aim: The present study was carried out to compare clinically and radiographically the efficacy of regenerative potential of Demineralizedfreeze-dried bone allograft (DFDBA) and Bioactive glass putty (Novabone© dental putty) in mandibular grade II furcation defects. Methods: In 34 Patients, 60 mandibular grade II furcation defects were treated using DFDBA and Bioactive glass putty. 30 furcations were treated using DFDBA, while bioactive glass putty was used to treat remaining 30 furcation defect. Clinical parameters evaluated were Plaque index (PI), Gingival index (GI), Probing pocket depth (PPD), Relative vertical attachment level (RVAL), Relative horizontal attachment level (RHAL) at baseline, 3 months and 6 months. Radiographic parameters recorded were linear measurement of defect depth and bone density in gray scale at baseline and 6 months. Results: Both the group showed significant reduction in mean (P≤0.05) GI, PI, PPD, RVAL and RHAL at 6 months. Group I showed greater reduction in PPD. Radiographic evaluation showed significant (P≤0.05) reduction in defect depth and increase in bone density in both groups. Conclusions: Bioactive glass putty showed comparable regeneration to that of DFDBA in the treatment of mandibular grade II furcation defect. Additional putty consistency of bioactive glass makes it easier and more convenient to use.

An Accurate and Efficient Fitness-For-Service Assessment Method of Pipes with Defects under Surface Load

With continued urbanization in China, the construction of urban gas pipelines is increasing, and the safety of gas pipelines are also increasingly affected by urban development and the increased scope of buildings and roads. Pipes with defects are more likely to fail under the surface loads. In this study, uniaxial tensile tests of high-density polyethylene (HDPE) pipes were carried out to obtain the real material parameters of pipe. A pipeline-soil interaction finite element model of HDPE pipeline with defects under surface load was established. The failure mechanism of the urban gas pipeline was studied and the influence of parameters such as internal pressure, defect position, defect depth on the mechanical behavior, and failure of pipelines were analyzed. A failure criterion for HDPE pipes with defects under surface load was proposed based on the limit-state curves obtained under different working conditions. Furthermore, an accurate and efficient fitness-for-service assessment procedure of pipes with defects under surface load was proposed. The results showed that maximum Mises stress of the pipeline gradually increased with increasing surface load and the position of maximum stress changed from the top and bottom of the pipe to the defect position and both sides of the pipe. Finally, when Mises stress of the HDPE pipe exceeds the yield limit, failure will occur. Internal pressure, defect location, and defect depth were found to influence the failure process and critical surface load of the pipeline. Safety evaluation curves of the gas pipeline with defects under surface load were obtained by calculating the critical failure load of the pipeline under various working conditions. Finally, a nonlinear fitting method was used to derive a formula for calculating the critical surface load under different defect parameters. The proposed method provides a useful reference for urban gas pipeline safety management.

A Method of Defect Depth Recognition in Active Infrared Thermography Based on GRU Networks

Active infrared thermography (AIRT) is a significant defect detection and evaluation method in the field of non-destructive testing, on account of the fact that it promptly provides visual information and that the results could be used for quantitative research of defects. At present, the quantitative evaluation of defects is an urgent problem to be solved in this field. In this work, a defect depth recognition method based on gated recurrent unit (GRU) networks is proposed to solve the problem of insufficient accuracy in defect depth recognition. AIRT is applied to obtain the raw thermal sequences of the surface temperature field distribution of the defect specimen. Before training the GRU model, principal component analysis (PCA) is used to reduce the dimension and to eliminate the correlation of the raw datasets. Then, the GRU model is employed to automatically recognize the depth of the defect. The defect depth recognition performance of the proposed method is evaluated through an experiment on polymethyl methacrylate (PMMA) with flat bottom holes. The results indicate that the PCA-processed datasets outperform the raw temperature datasets in model learning when assessing defect depth characteristics. A comparison with the BP network shows that the proposed method has better performance in defect depth recognition.

Deep Learning Automated System for Thermal Defectometry of Multilayer Materials

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 %.