Stress affects the microstructure of the material to influence the durability and service life of the components. However, the previous work of stress measurement lacks quantification of the different variations in time and spatial features of micromagnetic properties affected by stress in elastic and plastic ranges, as well as the evolution of microstructure. In this paper, microstructure evolution under stress in elastic and plastic ranges is evaluated by magnetic Barkhausen noise (MBN) transient analysis. Based on a J-A model, the duration and the intensity are the eigenvalues for MBN transient analysis to quantify transient size and number of Barkhausen events under stress. With the observation of domain wall (DW) distribution and microstructure, the correlation between material microstructure and MBN transient eigenvalues is investigated to verify the ability of material status evaluation on the microscopic scale of the method. The results show that the duration and the intensity have different change trends in elastic and plastic ranges. The eigenvalue fusion of the duration and intensity distinguishes the change in microstructure under the stress in elastic and plastic deformation. The appearance of grain boundary (GB) migration and dislocation under the stress in the plastic range makes the duration and the intensity higher on the GB than those inside the grain. Besides, the reproducibility of the proposed method is investigated by evaluating microstructure evolution for silicon steel sheet and Q235 steel sheet. The proposed method investigates the correlation between the microstructure and transient micromagnetic properties, which has the potential for stress evaluation in elastic and plastic ranges for industrial materials.

Low excitation voltage (LEV) electromagnetic ultrasonic detection is needed for the safety of high-temperature boilers, reactors, pipelines, and other equipment. However, the low signal intensity and high noise due to the LEV and the high-temperature environment result in a low signal-to-noise ratio (SNR) in the defect detection process. This study proposes a novel noise reduction method, called VMD-SVD-CEEMD (VSCE), which utilizes variational mode decomposition (VMD), singular value decomposition (SVD), and complementary ensemble empirical mode decomposition (CEEMD) to extract defect information and suppress pulsating noise, instrument noise, and high-temperature structural noise. The shear wave generated by an electromagnetic acoustic transducer was used to detect two different materials at 25V LEV, with a temperature range of 25°C to 700°C. The VSCE method was found to improve the SNR of received signals by 2-3 times compared to conventional denoising methods such as wavelet threshold denoising (WTD), VMD, and empirical mode decomposition (EMD). The positioning error of the defect location is less than 2.06%, which has proven the great application prospects and value of the VSCE methods in the LEV detection of high-temperature equipment.

Ultrasonic guided wave monitoring is regularly used for monitoring the structural health of industrial pipes, but small defects are difficult to identify owing to the influence of the environment and pipe structure on the guided wave signal. In this paper, a high-sensitivity monitoring algorithm based on adaptive principal component analysis (APCA) for defects of pipes is proposed, which calculates the sensitivity index of the signals and optimizes the process of selecting principal components in principal component analysis (PCA). Furthermore, we established a comprehensive damage index (K) by extracting the subspace features of signals to display the existence of defects intuitively. The damage monitoring algorithm was tested by the dataset collected from several pipe types, and the experimental results show that the APCA method can monitor the hole defect of 0.075% cross section loss ratio (SLR) on the straight pipe, 0.15% SLR on the spiral pipe, and 0.18% SLR on the bent pipe, which is superior to conventional methods such as optimal baseline subtraction (OBS) and average Euclidean distance (AED). The results of the damage index curve obtained by the algorithm clearly showed the change trend of defects; moreover, the contribution rate of the K index roughly showed the location of the defects.