Magnetic flux leakage (MFL) detection methods are widely used to detect pipeline defects. However, it is limited by the detection orientation and magnetization. Besides, bulky excitation systems are incapable of adapting to the complex detection environments. This article proposes a new electromagnetic structured coupling sensing of merging alternating current field measurement and MFL within a multiparameter system for different types of pipeline defects detection. In particular, a novel electromagnetic coupling sensor structure is proposed, which enables simultaneous interaction between the excitation modes of yoke and coil. Magnetic yoke is integrated to magnetizing the axial pipeline to detect the circumferential surface and subsurface defects while the coil excites the circumferential uniform alternating current field and recognizes the axial defect. The novel structured sensing is highly sensitive to the detection of both surface and subsurface defects. Simulation and experiments on defects in several samples have been conducted to validate the reliability and efficiency of the proposed system.

Eddy current testing (ET) and eddy current thermography (ECT) are both important nondestructive testing methods that have been widely used in the field of conductive materials evaluation. Conventional ECT systems have often employed to test static specimens even though they are inefficient when the specimen is large. In addition, the requirement of high-power excitation sources tends to result in bulky detection systems. To mitigate these problems, in this article, a moving detection mode of multiphysics structured ET and ECT is proposed in which a novel L-shape ferrite magnetic yoke circumambulated with array coils is designed. The theoretical derivation model of the proposed method is developed which is shown to improve the detection efficiency without compromising the excitation current by ECT. The specimens can be speedily evaluated by scanning at a speed of 50–250 mm/s while reducing the power of the excitation current due to the supplement of ET. The unique design of the excitation-receiving structure has also enhanced the detectability of omnidirectional cracks. Moreover, it does not block the normal direction visual capture of the specimens. Both numerical simulations and experimental studies on different defects have been carried out and the obtained results have shown the reliability and detection efficiency of the proposed system.

An edge displacement sensor is one of the key technologies for building large segmented mirror astronomical optical telescopes. A digital interface is one novel approach for sensor technologies, digital transformation and the Internet of Things (IoT) in particular. Frequency output sensors and inductance-to-digital converter (LDC) demonstrated significant advantages in comparison with conventional sensors with analog-to-digital converter (ADC) interfaces. In order for the differential inductive frequency output displacement (DIFOD) sensor to meet the high-stability requirements of segmented mirror astronomical telescopes, it is important to understand the factors for time drift of the sensor. This paper focuses on the investigation of key factors of sensor structure and material, signal conditioning and interface, and fixtures for time drift to permanently installed applications. First, the measurement principle and probe structural characteristics of the sensor are analyzed. Then, two kinds of signal conditioning and digitalization methods using resonance circuits and LDC chips are implemented and compared. Finally, the time drift stability experiments are performed on the sensors with different signal conditioning methods and fixtures under controlled temperature. Experimental results show that the magnetic shield ring effectively improves the sensitivity and quality factor of the sensors, the time drift stability of the sensor using the signal conditioning based on resonance circuits is better than that of the sensors using LDC chips, and the root mean square (RMS) of the sensor time drift meets the requirement of 0.01 μm/24 h. This study will help further development of high-stability of frequency output sensors and IoT-based systems for scaled-up applications in the future.

Conventional anomaly detection methods typically rely on training normal samples without defects to identify deviations from the background. However, these methods suffer from issues such as missing-detection or false-detection. Although subsequent research has attempted to improve algorithm performance, this often results in overfitting to specific cases within the anomaly detection framework. To address these challenges, this paper proposes a semi-supervised anomaly detection algorithm that combines improved metric learning techniques with a memory bank update module. In order to enhance the algorithm's generalization capabilities across different cases, a multi-cases memory bank inheritance approach is introduced. This approach facilitates rapid generalization to unknown test cases with minimal iterative learning (≤5 epochs). Additionally, a bank-case matching module is designed to select the appropriate memory bank and calculate anomaly scores within our framework. The effectiveness of the proposed algorithm has been validated through real industrial tests and ablation experiments, demonstrating its capability in detecting anomalies accurately and reliably. Code is available on: https://github.com/FrankCloud-UESTC/Mnlti-case-AD.