scholarly journals Wireless power transfer-based eddy current non-destructive testing using a flexible printed coil array

Author(s):  
Lawal Umar Daura ◽  
GuiYun Tian ◽  
Qiuji Yi ◽  
Ali Sophian

Eddy current testing (ECT) has been employed as a traditional non-destructive testing and evaluation (NDT&E) tool for many years. It has developed from single frequency to multiple frequencies, and eventually to pulsed and swept-frequency excitation. Recent progression of wireless power transfer (WPT) and flexible printed devices open opportunities to address challenges of defect detection and reconstruction under complex geometric situations. In this paper, a transmitter–receiver (Tx–Rx) flexible printed coil (FPC) array that uses the WPT approach featuring dual resonance responses for the first time has been proposed. The dual resonance responses can provide multiple parameters of samples, such as defect characteristics, lift-offs and material properties, while the flexible coil array allows area mapping of complex structures. To validate the proposed approach, experimental investigations of a single excitation coil with multiple receiving coils using the WPT principle were conducted on a curved pipe surface with a natural dent defect. The FPC array has one single excitation coil and 16 receiving (Rx) coils, which are used to measure the dent by using 21 C-scan points on the dedicated dent sample. The experimental data were then used for training and evaluation of dual resonance responses in terms of multiple feature extraction, selection and fusion for quantitative NDE. Four features, which include resonant magnitudes and principal components of the two resonant areas, were investigated for mapping and reconstructing the defective dent through correlation analysis for feature selection and feature fusion by deep learning. It shows that deep learning-based multiple feature fusion has outstanding performance for 3D defect reconstruction of WPT-based FPC-ECT. This article is part of the theme issue ‘Advanced electromagnetic non-destructive evaluation and smart monitoring’.

Author(s):  
Ling Miao ◽  
Bin Gao ◽  
Haoran Li ◽  
Guiyun Tian

Eddy current pulsed thermography (ECPT) has been widely used in the field of non-destructive testing due to its safety, non-contact detection, high spatial resolution and intuitive results. Inductive excitation source is an important component of ECPT and provides high-frequency alternating current to drive the excitation coil. However, a resonant frequency distortion phenomenon exists in the excitation source during the detection process, which seriously affects the output power of the excitation source and the sample detection effect. This paper presents a fast resonant frequency tracking loop for full bridge series resonant inverter which is used to search the resonance frequency in real time through direct digital synthesizer (DDS) and all-digital phase-locked loop. Theoretical analysis and simulation are presented to explain the working principle of the loop. Then, an experimental prototype is manufactured which serves as an excitation source for the ECPT experimental system. Compared with traditional excitation sources, the prototype does not need a water-cooled device and the tracking speed can be adjusted by modifying the parameters of DDS. Finally, experiments have been conducted on both artificial slot of 45# steel and natural cracks of rail and stainless steel to investigate the influence of resonant frequency tracking speed on the crack detection. The results revealed that reducing the resonant frequency tracking time can efficiently improve defect detectability and the manufactured prototype showed more application potential. This article is part of the theme issue ‘Advanced electromagnetic non-destructive evaluation and smart monitoring’.


2021 ◽  
Author(s):  
Erik Rohkohl ◽  
Mathias Kraken ◽  
Malte Schönemann ◽  
Alexander Breuer ◽  
Christoph Herrmann

Abstract Battery cells are central components of electric vehicles. It is important for automotive OEM to utilize high quality battery cells to ensure high performance and safety of their vehicles. This results in the high demand for quality control measures and inspection methods in battery cell manufacturing. Particular relevant features of battery cells are welds for the internal electrical contact. Failures of these welds are often the cause for battery defects in the field and scrap during production. Consequently, there is a strong need to evaluate all welds during manufacturing. However, there is no established method which allows a quick, comprehensive, and cheap inline measurement of the weld quality. This paper presents a new eddy current based method for non-destructive testing of seam welds as well as a machine learning approach for its validation. A deep learning model has been trained on eddy current measurements to predict results from a reference inspection method, in this case computer tomography. The results prove that eddy current measurements can be used to replicate data acquired by computer tomography which means that eddy current measurements could be a suitable candidate for non-destructive 100% inline inspection. More general, this study demonstrates how machine learning may help to get deeper insights into measurement results and to validate new non-destructive testing techniques whose detailed features are yet unknown. The presented evaluation method enables understanding the capabilities and the limits of a new technique and to extract hidden features from the data. Furthermore, the usage of machine learning allows to perform these evaluations on artificial product samples with specific defects and features, which avoids the costly production physical samples.


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