The magnetic flux leakage (MFL) inspection technology is widely used in pipeline industry to detect defects to ensure pipeline safety. During the high-speed inspection, a relative motion occurred between the pipeline inspection gauge (PIG) and the steel pipe wall will generate motion-induced eddy current (MIEC). There is a lack of research on analyzing the effect of MIEC on high-speed MFL inspection for thick-wall steel pipe. In this article, a three-dimensional (3D) finite-element method (FEM) simulations are conducted with an inspection speed range of 0 m/s to 8 m/s and a wall thickness range of 8 mm to 15 mm. Simulation results show that both high-speed inspection and thick-wall thickness will decrease the magnetization of steel pipe. It is observed that, at the speed of 8 m/s and the thickness of 15 mm, the magnetic field strength is lower than 2 kA/m and the steel pipe exits from the magnetic saturation zone, which causes the severe distortion of three-axis MFL signals, and the signal-to-noise ratio (SNR) is lower than 6 dB. A high-speed PIG is developed here for experiments to measure the three-axis MFL signals. The characteristics of simulated and measured MFL signals are found to be quite consistent.

Real-time tracking of pipeline inspection gauges (PIGs) is an important aspect of ensuring the safety of oil and gas pipeline inline inspections (ILIs). Transmitting and receiving extremely low frequency (ELF) magnetic signals is one of the preferred methods of tracking. Due to the increase in physical parameters of the pipeline including transportation speed, wall thickness and burial depth, the ELF magnetic signals received are short transient (1-second duration) and very weak (10 pT), making the existing above-ground-marker (AGM) systems difficult to operate correctly. Based on the short transient very weak characteristics of ELF signals studied with a 2-D finite-element method (FEM) simulation, a data fusion model was derived to fuse the envelope decay rates of ELF signals by a least square (LS) criterion. Then, a fast-decision-tree (FDT) method is proposed to estimate the fused envelope decay rate to output the maximized orthogonal signal power for the signal detection through a determined topology and a fast calculation process, which was demonstrated to have excellent real-time detection performance. We show that simulation and experimental results validated the effectiveness of the proposed FDT method, and describe the high-sensitivity detection and real-time implementation of a high-speed PIG tracking system, including a transmitter, a receiver, and a pair of orthogonal search coil sensors.

In this paper, a novel sub-carrier and power allocation algorithm for downlink transmission of multi-user OFDM system is presented. The proposed algorithm tries to minimize the total transmission power, while satisfying the bit error rate constraint and rate requirement of each user. The proposed algorithm combines the sub-carriers and power allocation simultaneously, by comparing the power increment to swap the sub-carriers between users. As demonstrated in the simulation results, the proposed algorithm offers better performance than other sub-optimal algorithms, with the similar computational complexity.

Magnetic induction tomography (MIT) is a method of electromagnetic parameter reconstruction in the object region by measuring the magnetic field and the induced voltage. The governing equations for the forward problem in MIT is derived to apply FEM and the forward problem is solved. In order to solve the inverse problem, a fast algorithm for the calculation of the sensitivity matrix is derived and the numerical simulation is carried out. The conductivity distribution is reconstructed and it is verified that the presented algorithm is computationally efficient and correct.

The estimation and detection of a weak magnetic dipole signal is a critical problem in magnetic target detection. The difficulty arises due to a latent variable in the model, which affects the estimation and detection performance, especially at low signal-to-noise ratios (SNRs). A non-probability-distribution expectation maximization (NPD-EM) algorithm is proposed to estimate the magnetic dipole signal with the latent variable at low SNRs. A reasonable value of an intermediate variable instead of the optimal one is determined without any probability information in the iteration of the NPD-EM algorithm, which overcomes an unknown probability distribution appearing in the traditional expectation maximization (EM) algorithm and reduces the calculated amount by 3 orders of magnitude compared with the traditional EM algorithm. A statistic based on the NPD-EM algorithm representing an unbiased estimator of the target signal energy is constructed to detect the magnetic dipole signal at low SNRs, and an innovative compensation in the detector is introduced so as to reduce the noise influence on the statistic. The experiment results show that, the constructed detector is comparable to the ideal matching filter due to the attractive performance of the NPD-EM algorithm and the outstanding statistic.

The digitalization level of the new power system driven by “dual carbon” is increasing, leading to a growth in the amount of data that need to be acquired. This has intensified the contradiction between data volume and acquisition capacity. Therefore, it is urgent to study compressed data acquisition methods for power systems based on data compression. In this regard, a novel compressed data acquisition method based on chaotic compressive measurement with the compressed sensing principle is proposed. Firstly, the advantages of applying compressed sensing are analyzed for data acquisition in power systems, and the key issues that need to be addressed are identified. Subsequently, a chaotic map is sampled based on the basic requirements of the measurement matrix in compressed sensing, and the chaotic compressive measurement matrix is constructed and optimized based on the sampling results. Next, the sparse data difference of the power system is used as the compression target for the optimized chaotic measurement matrix, and an acquisition process is designed to recover the complete power data from a small amount of compressed data. Finally, the proposed method is validated in a case study, and the results demonstrate that the method is correct and effective.