Методика на основе импульсных вихревых токов для определения механических свойств ферромагнитных материалов

We study the interaction of pulsed eddy current (PEC) with metal surface cracks using various coils that have different geometric sizes. In the previous work, we have showed that the PEC technique can be used to inspect electrical-discharge-machined (EDM) notches with depth from 0.5 mm to 9 mm. The results showed that the relationship between PEC signals and crack depth is obvious. In this work, we further try a series of coils with different radii, heights, turns and shapes. We will discuss the effects of these coil parameters on the PEC signal. Some other critical problems of PEC measurements such as signal drift that caused by heating effect of coil currents will be studied. We also show more experiments on fatigue cracks to demonstrate the capability of PEC technique for cracks inspection.

Pulsed eddy current (PEC) testing technology has been widely used in the field of non-destructive testing of metal grounding structures due to its wide-band excitation and response characteristics. However, multi-source noise in industrial environments can significantly degrade the performance of PEC sensors, thereby limiting their detection accuracy. This study proposes a multi-modal joint pulsed eddy current signal sensor denoising method that integrates the inductive disturbance mechanism. This method constructs the Improved Whale Optimization -Variational Mode Decomposition-Singular Value Decomposition-Wavelet Threshold Denoising (IWOA-VMD-SVD-WTD) fourth-order processing architecture: IWOA adaptively optimizes the VMD essential variables (K, α) and employs the optimized VMD to decompose the perception coefficient (IMF) of the PEC signal. It utilizes the correlation coefficient criterion to filter and identify the primary noise components within the signal, and the SVD-WTD joint denoising model is established to reconstruct each component to remove the noise signal received by the PEC sensor. To ascertain the efficacy of this approach, we compared the IWOA-VMD-SVD-WTD method with other denoising methods under three different noise levels through experiments. The test results show that compared with other VMD-based denoising techniques, the average signal-to-noise ratio (SNR) of the PEC signal received by the receiving coil for 200 noise signals in different noise environments is 24.31 dB, 29.72 dB and 29.64 dB, respectively. The average SNR of the other two denoising techniques in different noise environments is 15.48 dB, 18.87 dB, 18.46 dB and 19.32 dB, 27.13 dB, 26.78 dB, respectively, which is significantly better than other denoising methods. In addition, in practical applications, this method is better than other technologies in denoising PEC signals and successfully achieves noise reduction and signal feature extraction. This study provides a new technical solution for extracting pure and impurity-free PEC signals in complex electromagnetic environments.

The time-to-peak serves as a popular signal feature of pulsed eddy current (PEC) signals and is widely used in thickness measurement and defect detection. In order to further improve the ability of time-to-peak independent of the lift-off effect, a time derivative processing method is proposed in this paper to obtain a better time-to-peak feature of time derivative signals of PEC for reducing the lift-off effect. The method is used to improve the thickness measurement accuracy of ferromagnetic samples. The results of simulation and experimentation demonstrate that a time-to-peak feature of time derivative signals of PEC can be obtained using the time derivative processing method and the timeto-peak obtained from time derivative signals of PEC can be used to measure the thickness and improve the thickness measurement accuracy of ferromagnetic samples. This means that the use of the time-to-peak of time derivative signals of PEC is feasible for high-precision evaluation of the thickness of ferromagnetic samples.

It is difficult detecting flaws in multi-layered structures such as aircraft fuselage lap splices. Pulsed eddy current (PEC) is an effective method that has been demonstrated to be capable of quantifying such defects. Different from conventional eddy current testing techniques, the exciting coil of PEC is fed by a pulsed waveform, the transient response signal in time domain is analysed, peak amplitude and zero-crossing times are extracted to detect and characterise flaws. PEC is used to quantify cracks found in aircraft structure, with the purpose of overcoming the problem of low accuracy of PEC in the classification of defects. A new feature, namely the frequency spectrum separating. point, is proposed for defect classification. Finally, an array PEC imaging method is proposed to detect corrosion appearing in the second layer of aircraft structure.

Chapter 2 - Magnetic Sensor Based Pulsed Eddy Current for Defect Detection and Characterization

In order to know the effect of surface irregularity in the detection of local wall thinning of pipeline using pulsed eddy current (PEC), the lift-off effects on PEC signal have been investigated. Three kinds of parameters in the PEC signal, which is "peak amplitude", "time to peak amplitude" and "time to zero crossing" are analyzed to separate the lift-off effects in the PEC signal. The distance from sensor to the bottom of sample which is the total thickness of combined insulator and sample is kept constant. The magnitude of the differential peak amplitude is increased with increasing sample thickness, the time to peak amplitude is increased with increasing the sample thickness. To determine the effect of lift-off, a number of balanced transient responses combining wall thinning locations and lift-off distances were plotted.

In the current iron and steel industry production, the detection of the yield strength of ferromagnetic materials relies on loss detection. By determining the yield strength of the material through tensile tests, the material needs to be stretched to the yield limit, which greatly increases the cost of testing. This paper presents a method for estimating the yield strength parameters of ferromagnetic materials based on pulsed eddy currents. Firstly, the characteristics of time-domain and frequency-domain of pulsed eddy current response signals are extracted. Then, the back propagation neural network model of each signal characteristic and material yield strength is established, and finally the neural network is used to predict the yield strength of the material. Using the BP neural network model trained by features of pulsed eddy current, the yield strength of the specimens can be estimated. As a non-destructive testing method, the method has a prediction error of 5% or less, and has a certain practical value for reducing the detection cost of industrial production and improving the detection efficiency.

Due to a harsh working environment, micro-cracks in metal structures (e.g., airplane, oil/gas pipeline, hydro-turbine) often lead to serious accidents, so health monitoring of the metals is of great significance to ensure their safe operation. However, it is hard to perform quantitative detection of multiple micro-cracks by a single nondestructive testing (NDT) technique because of their limits. To monitor for multiple micro-cracks in metals, a Traditional Eddy Current (TEC) and Pulsed Eddy Current (PEC) fusion NDT technique is proposed in this paper. In the proposed technique, the TEC technique is adopted to seek the locations of the micro-cracks in the whole of the metal, while the PEC technique is adopted to acquire information on the depth of micro-cracks automatically according to the location information by the TEC. The experiments indicate that the TEC–PEC fusion NDT system can localize the micro-cracks as well as detect the micro-cracks quantitatively and automatically; therefore, it can be applied in structural health monitoring of metal equipment or in picking candidate components in re-manufacturing engineering.

An array type probe for monitoring metal tubes is proposed in this paper which utilizes peak value and peak time of a pulsed eddy current(PEC) signal. The probe consists of an array of encircling coils along a tube and the outside of coils is shielded by ferrite to prevent source magnetic fields from directly affecting sensor signals since it is the magnetic fields produced by eddy currents that reflect the condition of metal tubes. The positions of both exciter and sensor coils are consecutively moved automatically so that manual scanning is not necessary. At one position of send-receive coils, peak value and peak time are extracted from a sensor PEC signal and these data are accumulated for all positions to form an array type peak value signal and an array type peak time signal. Numerical simulation was performed using the backward difference method in time and the finite element method for spatial analysis. Simulation results showed that peak value increases and the peak appears earlier as the defect depth or length increases. The proposed array signals are shown to be excellent in reflecting the defect location as well as variations of defect depth and length within the array probe.

The pulsed eddy current (PEC) technique is widely used to detect and quantify flaws in conducting plates .This method has been used to test materials subjected to heat treatment. Material properties such as conductivity show a marked change with temperature. As the heat diffuses into the material, the probe output changes owing to the changing material properties. Hence, it becomes difficult to obtain consistent results when searching for defects. Such problems can be effectively tackled by combining thermal and pulsed eddy current analysis for getting a reference data for the signals acquired from the probe. In this paper, a model is presented which combines transient thermal analysis and PEC Finite Element Model to track the probe output changes occurring during Laser based heat treatment of thin sheet conducting materials. The first step encompasses modeling of the laser treatment process using a three dimensional Finite Difference method .Properties of the material such as conductivity and specific he...

The pulsed eddy current (PEC) method was used to inspect metal surface cracks that with depth ranging from 0.5 mm to 9 mm. Various of metals, including non-magnetic and magnetic, were tested. We measured the EDM cracks using two different size probes. The comparison for various combinations of crack depths, probes, and materials were showed. Results show that there is a strong relationship between the peak signal amplitudes and crack depths. It is evident that the PEC technique is a potential method for quantitatively determination of deep surface cracks.

Pulsed eddy current technique for defect detection in aircraft riveted structures

Corrosion characterisation of coated steel involves various approaches via Non- destructive Testing (NDT) and Structural Health Monitoring (SHM) techniques. This paper evaluates and discusses the application of the recent development of a portable lamp-pulsed thickness tester and Commercial Off-the-Shelf (COTS) ultra-high frequency (UHF) radio frequency identification (RFID) sensing. The findings affirm the efficacy of the portable lamp-pulsed thickness tester and COTS UHF RFID sensing for corrosion characterization. Furthermore, these methods are compared with other NDT&E and SHM techniques such as pulsed eddy current (PEC), eddy current pulsed thermography (ECPT), low frequency (LF) RFID, and chipless RFID sensing and monitoring. This study compares the principles, merits, limitations, and accuracy of different corrosion characterization approaches and discusses their future development.

A resonant frequency retrieving method for low Q-factor materials based on resonant ultrasound spectroscopy

With the development of nondestructive detection, the emerging testing techniques provide new challenges to signal analysis and interpretation approach applied to the inspection evaluation. Some researchers have developed the methods that focus on feature analysis of detected signals. This article presents a new feature analysis by the Independent Component Analysis (ICA) approach to evaluate the defects tested by the pulsed eddy current (PEC) technique. ICA is a high-order statistics technique used to separate multi-unknown sources, which has been successfully applied to facial image identification and separation of the components of 1D signal. In this article, the ICA approach is utilized to project the response signals of various defects into the independent components (ICs) feature subspace by signal representation model. Dependent on the selected ICs, each defect is represented by different projected coefficients, which are proposed to discriminate and classify the defects that belong to three categories. The improved ICA model is proposed to improve the classification of two similar categories of single defects: metal loss and subsurface defects. The evaluation using the series of experimental data has validated the classification of single defects and the defects with lift-off effect by our ICA approach. The comparison with Principal component analysis (PCA)–based approach further verified the better performance of the ICA-based model.