Magnetic flux leakage (MFL) testing is coming into prominence as an inspection tool applied to pipeline evaluation. The MFL detectors are challenging to accurately evaluate pipe defects at high speed due to velocity effects. Current methods typically require detectors to operate at low speeds or compensate for detection data offline, but they reduce detection efficiency or require sophisticated algorithms. In this article, a novel double remote magnetic field synthesis (DRMFS) method is proposed to address the issue. First, we give a thorough analysis of the influence of velocity effects on high-speed MFL signals, reinterpreting its action mechanism. Second, the critical parameters are optimized to improve pipe magnetization. The relationships between the sensor layout and normalized signal similarity are studied at different speeds to reduce signal distortion. Finally, the usability of the DRMFS method in reducing signal distortion and its capability in detecting deep defects are validated by measuring defect depths. Results showed synthesis signals are highly similar to static MFL signals and approximately linear with the defect depth, which could improve the detection accuracy. Our results can enable the exploration of information utilization in limited space and the establishment of a balance between detection efficiency and signal accuracy.

Becausethe fluctuation and uncertainty of wind power generation bring severe secure and economic challenges to power systems, wind power forecasting becomes the critical part of the management in power systems. In this paper, a hybrid approach based on unequal span segmentation-clustering is proposed to mine the variation trend information in the wind speed series for improving forecasting performance. Firstly, the wind speed series representation is proposed to accurately represent the major variation trend of the wind speed series. It reduces the influence of the non-stationary and irregular behaviors of the wind speed series on the unequal span segmentation-clustering. Secondly, the shape-position comprehensive evaluation is proposed to combine shape and position measures to evaluate the clustering of the trend segments with unequal length. It identifies the trend segments which have comprehensive similarity on both shape and position to better construct forecasting models. Thirdly, novel bayesian optimization mutation operators are proposed to optimally move or add cut points in the unequal span segmentation. It enhances the local search capability of the unequal span segmentation. By comparing different approaches using wind datasets from two wind farms, the effectiveness and advantages of the proposed approach are demonstrated.

In the production and service of pipelines, automated electromagnetic detection is widely employed to diagnose potential defects. However, the single inspection method can hardly meet reliability and multi-position detect detection simultaneously due to limited signal features. In this paper, a novel composite approach of electromagnetic acoustic transducer and eddy current is proposed to detect different kinds of defects combining their technical merits. A comprehensive theoretical analysis of the composite approach is given to derive the modified model of the send-receive mode. The influences of the critical parameters on hybrid signals are discussed and the probe performance is improved over 65% in magnitude. Then, the time-division multiplexing strategy is utilized to separate hybrid signals. Finally, laboratory experiments are conducted to extract independent features and diagnose potential defects in the steel plate. Results indicated that the proposed approach could achieve a good performance in the detection task of inner and outer defects. Compared with existing detection methods, our proposed approach has significant merits in detectability and efficiency.

The detection of surface and backside defects in steel plates is essential for safety maintenance. Current ac magnetic flux leakage (ACMFL) testing systems generally operate at a high frequency but cannot detect backside defects owing to the skin effect. Low-frequency ACMFL is limited by low sensitivity and low efficiency in detecting deep defects. This letter presents a multifrequency ACMFL testing method combined with the merits of low- and high-frequency excitation to detect surface and backside defects. In early work, the optimal frequency for detecting the surface defect with a depth of 4 mm or less was 400 Hz. Through simulation analysis, the optimal low frequency for detecting backside defects is determined. Additionally, the influence of the excitation intensity on the response signal is investigated. Relatively small excitation power should be used to obtain a reliable detection waveform. Finally, the experimental results of surface and backside defects detection validated the practicality of the method.

Rayleigh wave methods utilizing electromagnetic acoustic transducers (EMATs) are widely used to detect a crack defect on the metal surface due to the merits of high sensitivity and long detection distance. However, the current EMATs for Rayleigh wave has low energy conversion efficiency and usually produce a wide echo-wave packet, reducing the spatial resolution and signal-to-noise ratio of detection signals. To address the issues, the coil spatial pulse compression technique (CSPCT) is proposed to improve the physical structure of EMATs. First, a comprehensive analysis of the coil design is given to realize spatial phase encoding pulse compression. Second, the influence of the key parameters of EMATs on the pulse compression effect is studied. Finally, the crack detection experiment is conducted on an aluminum plate to verify the performance improvement of the designed EMAT. Compared to existing EMATs, our designed EMATs reduce the width of the main lobes and enhance the amplitude of the echo-wave signals. Results indicated that the spatial resolution was improved by up to 72% with an increase of 0.72 dB in signal-to-noise ratio. In comparison to the existing time-domain pulse compression technique, the CSPCT requires no complex hardware and software tools, which has the merits of low cost and fast response. This work expands the application boundary of EMAT systems.

AC magnetic flux leakage (ACMFL) testing is a novel method for the detection of defects of metallic materials. Conventional ACMFL probes excited by a single-frequency (SF) sine signal have limitations in accurately detecting the defect size and identifying the defect orientation. In this article, a multifrequency ACMFL (MF-ACMFL) method is proposed to address these issues. First, we give a thorough analysis of the effects of excitation frequencies on magnetic flux density under different defect sizes and orientations using the finite-element method. The numerical results indicate that the optimal frequencies are 400 and 500 Hz to detect the defect size, and that to identify the defect orientation are approximately 400-500 Hz. Then, we compare the receiver signal under SF and MF excitations and estimate the size of the defect including its length, width, and depth under oblique angles of 0° and 30°, whose results validate the effectiveness of the proposed method. Finally, measuring the depths and orientations of the defect on the thick steel plate verifies the correctness of the numerical results and the advantage of the proposed method in the orientation identification of the defect. Thus, the proposed scheme is an effective complementary measure for the existing ACMFL tools.