To achieve high-precision detection of magnetic anisotropy in ferromagnetic materials, this paper develops a magnetic anisotropy detection system based on enhanced time–frequency analysis. The hardware platform mainly consists of an excitation board, a power amplifier, a data acquisition card, sensors, and a host PC. The software is developed on the LabVIEW platform and integrates data acquisition, real-time signal processing, and feature analysis into a unified measurement and control system. Through frequency-sweep and amplitude-sweep experiments, the optimal operating parameter range of the sensor is determined, and the system repeatability is evaluated using the coefficient of variation of the measured data. The results demonstrate that the system exhibits good repeatability for 21 characteristic parameters, satisfying the requirements for high-precision detection. On this basis, the short-time Fourier transform is introduced into the anisotropy analysis of magnetic Barkhausen noise, and a multidimensional feature parameter system is established. Experimental comparisons between 30QG130 grain-oriented silicon steel and X70 pipeline steel indicate that the proposed time–frequency domain method exhibits higher sensitivity in quantifying the degree of magnetic anisotropy and identifying the rolling direction of materials than conventional time-domain methods. The anisotropy coefficient k reaches its maximum value in the critical magnetization region. Further experimental results show that different anisotropic mechanisms exhibit distinct response patterns in the time and frequency domains. Time–frequency analysis can directly reveal the magnetic domain magnetization dynamics by simultaneously observing time-domain evolution and frequency-domain energy distribution, thereby providing a novel and effective approach for decoupling complex magnetic anisotropy coupling mechanisms.
An alternative approach was proposed to detect the damage in cementing casing pipes by using circumferential guided wave detection technology. Firstly, the circumferential shear horizontal (C-SH) wave propagation problem of the steel pipe covered by semi-infinite cement was solved by the double polynomial expansion method (combining the Legendre polynomial expansion method and the Laguerre polynomial expansion method). Based on the typical application range corresponding to different polynomial types, theoretical modeling of wave behavior in the substrate and covering area is carried out separately. The dispersion equations of the C-SH wave in the cylindrical coordinate system can be derived based on the orthogonal completeness property. To verify the accuracy of the proposed method, a consistency analysis was conducted between the theoretical calculation results and available reference data. Meanwhile, a time domain simulation for cemented casings was established to confirm the feasibility of the proposed approach. Finally, the propagation characteristics of C-SH waves in cementing casing structures were experimentally analyzed, and the variation pattern of circumferential SH waves by cementation defects at different angles was also investigated. It provides a new research path for identifying typical defects in cementing casing.
Ground penetrating radar (GPR), often referred to as the “CT” of road, can be applied to conduct a comprehensive analysis of the internal structure and potential defects of roads, thereby providing an accurate assessment of its overall condition. However, current qualitative analysis of the collected radar data primarily relies on mapping, which limits the ability to effectively assess the varying degrees of damage caused by the underlying defects. In this paper, gprMax is used to model the forward simulation of GPR signals through asphalt pavement, focusing on cracks, interlayer defects, loose structures. The study explores the time-frequency characteristics of varying degrees of damage caused by these defects, aiming to analyze how the time-frequency eigenvalues change as the severity of the damage increases. A sensitivity analysis is conducted to examine the sensitivity of different time-frequency eigenvalues to variations in the degree of damage. The non-stationary characteristics of signals along both the time and frequency axes are analyzed using wavelet transform. Finally, the results of the forward simulation are validated against the measured data. The results show that five time-domain eigenvalues and four frequency-domain eigenvalues are effective in comprehensively characterizing varying degrees of damage caused by three types of defects: cracks, interlayer defects, and loose structures. Specifically, the trend of change in crack eigenvalues is opposite to that of interlayer defects and loose structures. Among the time-frequency eigenvalues, the square difference (SQD) and quadratic variance (QV) in the time domain, as well as the mean value (MV) and standard deviation (STD) in the frequency domain, exhibit greater sensitivity to the degree of road damage. As the degree of damage increases, the shifts in peak frequency for three defects differ. The peak frequencies of cracks, at 1.4 and 2.0 GHz, shift to higher frequencies, while the peak frequency of interlayer defects, at 2.0 GHz, shifts to lower frequencies. In contrast, the peak frequency of loose structures, at 1.4 GHz, shifts to higher frequencies. In summary, time-frequency characteristics offer an effective approach to analyzing varying degrees of damage caused by the internal road defects, thereby providing theoretical support for the advancement of ground penetrating radar detection from qualitative image recognition to quantitative analysis.
The overall strength of the cable depends on many factors such as aging, thermal effects and stress developed. In this work, the failure of conductor is determined by investigating its ability using a Non Destructive Method. The dielectric strength and stiffness of various Cross linked polyethylene (XLPE) cable for different mechanical properties are detected, monitored and analyzed using this method. Finally, a comparison is made between the measured results and the actual value of the pure specimen. The results describe that the development of Acoustic Emission (AE) technique is very efficient to test and evaluate the tensile performance of power cables and to ensure the integrity and safety of the underground power lines at any high voltage applications and during its maintenance. Also, it is observed that before breakdown these cables have the ability to elongate at a range of 150 to 200
The defects in solar panels reduce the solar energy production and hence the detection of these defects in solar panels is important. The timely intervention and inspection of the cells in the solar panels reduce the losses in the solar panels energy production process. This can be done by the automated solar panel inspection method. This research work proposes a fully automated Artificial Intelligence (AI) based solar panel crack detection system using Convolutional Neural Network (CNN) feature incorporated with Vision Transformer Model (VTM). The main objective of this work is to classify the drone captured solar panel images into either cracked or non-cracked. This proposed work is designed with the Anisotropic Diffusion (AD) filter, Multi-Window Gabor Transform (MWGT), Resource Optimized Depthwise Convolutional Neural Networks (RODCNN) architecture and Vision Transformer Model (VTM). AD filter is used to smooth the regions while suppressing noises in images. The MWGT enhances the time-frequency resolution property of each pixel in the AD filtered image and then a novel RODCNN architecture is designed for computing the solar panel features. The features are further classified using the VTM classification approach, which classifies the solar panel images into either cracked or non-cracked. This proposed methodology is tested and validated on two solar panel imaging datasets RoboFlow and Kaggle.The average computational crack detection results have been attained as 98.84
