The development of Koch curve fractal geometry provides valuable ideas for the structural design of sensor. Conventional eddy current sensor (CECS) can not effectively concentrate electromagnetic fields energy and have low detection sensitivity. The eddy current sensor (ECS) based on Koch curve fractal geometry can change the distribution of electromagnetic fields in space. Therefore, based on Koch curve fractal geometry, a novel ECS with multiple spatial degrees of freedom (SDOF), including torsion structure of x-y plane and inclination angle of y-z plane, for concentrating magnetic fields (MSECS) is developed in this paper. The proposed multiple SDOF are analyzed and optimized by finite element analysis. An energy spectrum function is proposed to describe the distribution of eddy current. The simulation results show that MSECS can concentrate electromagnetic fields and improve energy transfer efficiency. Defect detection experiments are conducted on prefabricated defective steel plates, which show that MSECS has better performance of detection.

Magnetic flux leakage (MFL) detection has been attracted much attention in evaluating the pipeline integrity due to its high efficiency. The precise calculation of the MFL signals plays an important role in the defect size quantification. Currently, finite element model (FEM) and magnetic dipole model (MDM) are two main methods, but FEMs are time-consuming and MDM cannot generate the dynamic MFL signals. To solve the above problems, a defect size quantification method for pipeline dynamic MFL detection via 3-D dynamic magnetic dipole forward model (3-D DMDFM) is proposed. First, a 3-D DMDFM is established, where the dynamic influencing factors, such as the speed, lift-off, and magnetization angle, are integrated into 3-D MDM, so that 3-D DMDFM can generate dynamic MFL signals. Second, to solve the problem of MFL signals distortion due to the dynamic influencing factors, a layer-skipping stacked denoising autoencoder (LS-SDAE) is proposed to reconstruct the MFL signals accurately, where two layer-skipping denoising autoencoders (DAEs) are used to supervise SDAE and learn the underutilized features from the nondirect connection feature layer. Simulated and experimental results show that 3-D DMDFM has both high efficiency and low error when ignoring the modeling complexity. Finally, the reconstructed MFL signals are used to quantify the defect size, and the minimum relative error of the defect depth quantification can reach 1.33%.

This article explores predictor-based fractional-order sliding mode load frequency control for interconnected power systems, accounting for input delay. First, a predictor-based method is developed to deal with the input delay. By designing a predictor to accurately predict the future state, the delayed control input can be replaced by the delay-free control input. Then, a novel fractional-order sliding mode controller is designed, where the predictor is used rather than the system state, reducing the dependence on directly measurable system states and enhancing the dynamic response of the system. Furthermore, a disturbance observer is designed to estimate the disturbance, allowing the controller to correspondingly compensate for it, and the robustness of the system is improved. Finally, three cases are conducted to show the validity of the presented method.

The ultrasonic B-scan signal is widely used in the detection of pipeline defects. However, most existing methods face challenges in achieving high accuracy due to the amplitude attenuation phenomenon and the complicated texture structure of the ultrasonic B-scan signal. To address the above issues, a texture simplification-based denoising diffusion model (TSDD-UB) is proposed for unsupervised defect detection under ultrasonic B-scan signal. This model closely follows the principles of ultrasonic propagation and incorporates meticulous designs in both data processing and model architecture, aiming to achieve high-precision defect detection while training exclusively on normal samples. First, an adaptive time gain compensation method is proposed to mitigate the amplitude attenuation phenomenon of the B-scan signal. Second, a novel texture simplification network is proposed to simplify texture structures while preserving the echo characteristics of the B-scan signal. Third, a double noise scale reconstruction method is designed, which utilizes the denoising diffusion model to reconstruct signals with two different levels of perturbation, aiming to overcome the issue of low reconstruction quality. Experimental results in real pipelines demonstrate that TSDD-UB achieves an image-level AUROC of 100% and a pixel-level AUROC of 98.6%, outperforming state-of-the-art methods and indicating significant potential for practical applications.

Magnetic flux leakage (MFL) detection has been widely used in the pipeline integrity evaluation. However, the MFL signals will be distorted due to the speed effect, which reduces the accuracy of the defect size inversion severely. To solve above problems, a multiple information-guided collaborative network (MIG-CN) is proposed to invert defect sizes accurately for the distorted pipeline MFL signals caused by the speed effect. First, a multiview feature fusion method is proposed, which enables MIG-CN to predict the defect sizes of specific types accurately by fusing the features of specific views, where the mechanism features are injected into the network to fully exploit the physical information of the MFL signals, improving the inversion accuracy. Second, a physics-informed constrained network is proposed, in which a speed magnetic dipole model is developed to impose constraints from real physical laws with the speed effect, thus enhancing the physical plausibility of the inversion results. Third, a dynamic-static collaborative decision strategy is proposed, in which a two-point constrained quantile probability modelling is proposed to model the inversion results of the undistorted MFL signals and obtain the prior probability distribution. The accuracy and robustness of the inversion results at different speeds are improved by minimizing the difference between the probability distribution of the inversion results of the distorted MFL signals and those of the undistorted. The above three components are jointly trained and optimized to improve the inversion accuracy under the influence of speed effect. Finally, the experiments on real pipeline networks verifies the effectiveness of MIG-CN.