Multi-Modal Joint Pulsed Eddy Current Sensor Signal Denoising Method Integrating Inductive Disturbance Mechanism

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.

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

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.

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.

This paper presents a pulsed eddy current (PEC) sensor design for cascade electrical conductivity and thickness estimation in non-ferrous metal plates. With a single excitation coil design, the combined change in magnetic flux density caused by the excitation coil and the induced eddy current (EC) in the test plate is detected by a compact anisotropic magneto-resistive (AMR) sensor. The effects of the varying input pulse width are discussed with both numerical simulations in commercial finite element analysis (FEA) software and experimental results, and the time-domain characteristics of the responses are analyzed in relation to the properties of the test plates. It is revealed that the electrical conductivity can be gauged based on the peak value of the short pulse response, and with a known electrical conductivity value, plate thickness can be subsequently derived with the rise time of the long pulse response. A cascade estimation process is therefore proposed to calculate the parameters sequentially by adopting hybrid input signals with both short and long input pulses. Verified by the FEA results, this cascade estimation process was then experimentally validated with a prototype of the PEC sensor.

In the current steel industry production, the detection of the mechanical properties of ferromagnetic materials relies on destructive testing, which greatly increases the cost of production. In this paper, a method is proposed to estimate the mechanical properties of ferromagnetic materials based on the pulsed eddy current (PEC) techniques. Firstly, the traditional features of the PEC signal, such as differential signal peaks and spectral amplitudes, are applied to the quantitative estimation of mechanical properties. Secondly, a method based on the empirical mode decomposition and Hilbert-Huang transform is used to extract the marginal spectral peak and marginal spectral energy as new features. Finally, BP neural network algorithm is introduced to quantitatively estimate the mechanical property parameters. The results show that the combination of both traditional and new features is suitable for mechanical properties estimation. The method has high accuracy for the quantitative estimation of mechanical performance parameters.

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.

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.

Pulsed Eddy Current (PEC) has been applied to characterise atmospheric corrosion on steel samples. Coated and uncoated mild steel (S275) samples with different marine atmospheric corrosion have been supplied by International Paint <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> . After introduction of pulsed eddy current systems, different measurement on different samples have been undertaken. Experimental results using pulsed eddy current sensor systems and feature extraction on corrosion classification and corrosion progression are reported. In conclusion, PEC testing has potential for corrosion characterisation and monitoring in industrial fields where corrosion is an important problem, e.g. the marine industry.

Nuclear steam generators (SGs) are a critical component for ensuring safe and efficient operation of a reactor. Life management strategies are implemented in which SG tubes are regularly inspected by conventional eddy current testing (ECT) and ultrasonic testing (UT) technologies to size flaws, and safe operating life of SGs is predicted based on growth models. ECT, the more commonly used technique, due to the rapidity with which full SG tube wall inspection can be performed, is challenged when inspecting ferromagnetic support structure materials in the presence of magnetite sludge and multiple overlapping degradation modes. In this work, an emerging inspection method, pulsed eddy current (PEC), is being investigated to address some of these particular inspection conditions. Time-domain signals were collected by an 8 coil array PEC probe in which ferromagnetic drilled support hole diameter, depth of rectangular tube frets and 2D tube off-centering were varied. Data sets were analyzed with a modified principal components analysis (MPCA) to extract dominant signal features. Multiple linear regression models were applied to MPCA scores to size hole diameter as well as size rectangular outer diameter tube frets. Models were improved through exploratory factor analysis, which was applied to MPCA scores to refine selection for regression models inputs by removing nonessential information.

Research on pulsed eddy current automatic detection technology for weld defects of pressure vessel cylinder

The performance and potential ambiguity in casing thickness evaluation using pulsed eddy current (PEC) measurement interpretation based on response time to depth mapping is assessed for realistic downhole settings. Casing eccentering sensitivity analysis based on 3D finite-element method is performed for PEC sensors of variable lengths. The effect of defect size, approaching defects and casing collars for three casing configurations is also studied to highlight the increased interpretation uncertainty in realistic noncentered environments.

This paper describes the challenges of detection and parameter estimation priori unknown multi-component radar signals with linear frequency modulation (LFM) in intense noise and complex jamming conditions. A method including two stages is proposed: The first stage is to detect signals, or in other words, estimate the chirp rate, and second is to estimate the pulse width of signals. The proposed approach is firstly examined by identifying the LFM signals in the environment of intense noise and mixing that noise with the continuous wave (CW) signal in MATLAB. An experiment with real-time LFM signals confirms that the method is able to detect and estimate the parameters of multi-component LFM signals in intense noise or in combining CW signal and that noise with a signal-to-noise ratio (SNR) greater than -18dB and -12dB, respectively.

The pulsed eddy current (PEC) system has been developed for the detection of the wall thinning in the pipelines of nuclear power plants (NPPs) without removing the insulation. The PEC signal response from the stainless steel sample which the thickness varies from 1 mm to 5 mm was investigated at the position of 6 mm above the sample surface. A LabVIEW based data acquisition program was developed to continuously display the thickness variation of the tested sample. The signal analysis technique such as power spectrum density (PSD) of Fourier transform is applied to obtain an optimum parameter to describe the wall thinning of pipeline steel. This technique can be used as a potential tool to detect the local wall thinning of a pipe without the removal of the insulation.

Thickness measurement using liftoff point of intersection in pulsed eddy current responses for elimination of liftoff effect