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1

Non-contact harmonic detection of ferromagnetic material defects based on SQGSR and OPLTF

Zhao Yizhen, Wang Xinhua, Chen Yingchun, Ju Haiyang, Shuai Yi

Metrology and Measurement Systems

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2021

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Vol. 28, nr 1

55--72

EN

In order to find the defects in ferromagnetic materials, a non-contact harmonic detection method is proposed. According to the principle of frequency modulated carrier wave, a tunnel magneto resistance harmonic focusing vector array detector was designed which radiates lower and higher frequency electromagnetic waves through the coil array to the detection targets. We use bistable stochastic resonance to enhance the energy of collected weak target signal and apply quantum computation and a Sobol low deviation sequence to improve genetic algorithm performance. Then we use the orthogonal phase-locked loop to eliminate the intrinsic background excitation field and tensor calculations to fuse the vector array signal. The finite element model of array detector and the magnetic dipole harmonic numerical model were also established. The simulation results show that the target signal can be identified effectively, its focusing performance is improved by 2 times, and the average signal-to-noise ratio is improved by 9.6 times after the algorithm processing. For the experiments, we take Q235 steel pipeline as the object to realize the recognition of three defects. Compared with the traditional methods, the proposed method is more effective for ferromagnetic materials defects detection.

2

Geomagnetic detection method for pipeline defects based on ceemdan and WEP-TEO

Zhang Tao, Wang Xinhua, Chen Yingchun, Shuai Yi, Ullah Zia, Ju Haiyang, Zhao Yizhen

Metrology and Measurement Systems

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2019

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Vol. 26, nr 2

345--361

EN

This paper presents a geomagnetic detection method for pipeline defects using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet energy product (WEP) - Teager energy operator (TEO), which improves detection accuracy and defect identification ability as encountering strong inference noise. The measured signal is first subtly decomposed via CEEMDAN into a series of intrinsic mode functions (IMFs), which are then distinguished by the Hurst exponent to reconstruct the filtered signal. Subsequently, the scale signals are obtained by using gradient calculation and discrete wavelet transform and are then fused by using WEP. Finally, TEO is implemented to enhance defect signal amplitude, completing geomagnetic detection of pipeline defects. The simulation results created by magnetic dipole in a noisy environment, indoor experiment results and field testing results certify that the proposed method outperforms ensemble empirical mode decomposition (EEMD)-gradient, EEMD-WEP-TEO, CEEMDAN-gradient in terms of detection deviation, peak side-lobe ratio (PSLR) and integrated side-lobe ratio (ISLR).

3

Defect recognition of buried pipeline based on approximate entropy and variational mode decomposition

Ju Haiyang, Wang Xinhua, Zhang Tao, Zhao Yizhen, Ullah Zia

Metrology and Measurement Systems

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2019

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Vol. 26, nr 4

739--755

EN

The study aimed to examine the use of Geomagnetic Anomaly Detection (GAD) to locate the buried ferromagnetic pipeline defects without exposing them. However, the accuracy of GAD is limited by the background noise. In the present work, we propose an approximate entropy noise suppression (AENS) method based on Variational Mode Decomposition (VMD) for detection of pipeline defects. The proposed method is capable of reconstructing the magnetic field signals and extracting weak anomaly signals that are submerged in the background noise, which was employed to construct an effective detector of anomalous signals. The internal parameters of VMD were optimized by the Scale–Space algorithm, and their anti-noise performance was compared. The results show that the proposed method can remove the background noise in high-noise background geomagnetic field environments. Experiments were carried out in our laboratory and evaluation results of inspection data were analysed; the feasibility of GAD is validated when used in the application to detection of buried pipeline defects.