Concrete structures have emerged as some of the most extensively utilized materials in the construction industry due to their inherent plasticity and high-strength characteristics. However, due to the temperature fluctuations, humidity, and damage caused by human activities, challenges such as crack propagation and structural failures pose threats to the safety of people’s lives and property. Meanwhile, conventional non-destructive testing methods are limited to defect detection and lack the capability to provide real-time monitoring and evaluating of concrete structural stability. Consequently, there is a growing emphasis on the development of effective techniques for monitoring the health of concrete structures, facilitating prompt repairs and mitigation of potential instabilities. This paper comprehensively presents traditional and novel methods for concrete structural properties and damage evolution monitoring, including emission techniques, electrical resistivity monitoring, electromagnetic radiation method, piezoelectric transducers, ultrasonic techniques, and the infrared thermography approach. Moreover, the fundamental principles, advantages, limitations, similarities and differences of each monitoring technique are extensively discussed, along with future research directions. Each method has its suitable monitoring scenarios, and in practical applications, several methods are often combined to achieve better monitoring results. The outcomes of this research provide valuable technical insights for future studies and advancements in the field of concrete structural health monitoring.

Magnetic metallic rods are widely used in the manufacture of turbine blades and other hot end components in aircraft engines, the material structure will be changed due to the impact of high temperature, high pressure, and corrosion environments [1, 2]. The structure of metallic rod can be characterized by the electromagnetic properties and size information [3]. In this paper, we have proposed a method to simultaneously reconstruct the permeability, conductivity and diameter using multi-frequency ECT system. The ECT coil encircles the metallic rod. According to the simplified classical Dodd-Deeds analytical solution, the relationship between the self-inductance variation of coil and parameters of metallic rods has been analyzed under different frequencies. Specifically, the real part of self-inductance variation is determined by both permeability and diameter at low-frequency. The effect of conductivity on self-inductance variation increases as the increasing frequency. As a supplementary information, the zero-crossover frequency, a feature at mid-frequency, can be used to invert conductivity. When the excitation frequency reaches high-frequency (~MHz), the influence of electromagnetic information can be ignored due to skin effect. Hence, the rod diameter can be directly inverted by self-inductance variation. Based on the above information, the permeability, conductivity and diameter can be derived, respectively. The experiments and FEM are also conducted to verify the proposed method. The results indicate the relative errors of permeability, conductivity and diameter are less than 3.0%.

State monitoring technology is essential for the fault prognostics and health management of power electronic devices. Electromagnetic voiceprint (EMVP) signals, produced during the switching transients of these devices, carry substantial information about their health status. In recent years, EMVP signals have gained attention as a promising state monitoring indicator for power electronic devices. However, the mechanism by which the health status of a device affects its EMVP signals remains unclear, limiting their use in health monitoring. This article investigates the impact of insulated gate bipolar transistor (IGBT) health status on EMVP signals and develops a mechanistic model to illustrate the influence of bond wire lift-off and solder aging on EMVP signals. Simulation models were developed to analyze the impact of both scenarios on EMVP signals. Typical features are proposed to characterize the relationship between IGBT health status and EMVP signal. Experimental validation supports the theoretical framework and simulation results. The research provides a theoretical foundation for the development of reliability evaluation methods based on EMVP.

As production speeds continue to increase, accurately measuring defects during the high-speed motion of samples becomes of significant practical importance. Motion-induced eddy current (MIEC) is a nondestructive testing method well-suited for detecting defects in high-speed moving samples. Existing studies typically employ permanent magnets or coils as excitation sources, without addressing the impact of the central magnetic field and magnetic field gradient of the excitation source on the generated MIEC signals. This study analyzes, both theoretically and through simulations, the influence of the central magnetic field and magnetic field gradient on MIEC signals. Based on these findings, a new excitation source structure is designed, and an improved multi-objective RIME optimization algorithm is proposed to optimize the shape of the magnetic yoke surrounding the permanent magnet. Numerical simulations demonstrate that the optimized excitation source structure significantly enhances the amplitude of defect signals, across various sample motion speeds. Finally, an experimental setup is constructed to validate the results. The experimental results indicate that the optimized excitation source structure effectively enhances the MIEC signal amplitude for five different slot-shaped defects ranging from 0.5 mm to 2.5 mm in length on the aluminum disk, with a minimum amplitude increase of 52% and a maximum of 74.1%.

During the cracking process of concrete, electromagnetic radiation (EMR) signals are emitted as a result of transient stress variations occurring on crack surfaces. By analyzing the characteristics of these EMR signals, researchers can monitor the health status of concrete structures. This study investigates the mechanical and electromagnetic properties of concrete during the cracking process while proposing mechanisms for piezoelectric and frictional charge generation on crack surfaces, based on the theory of electric dipole oscillations. Considering the effects of crack propagation velocity and surface stress, a model for the emission of EMR signals during the propagation of a single crack in concrete has been developed. The model correlates crack distance, applied stress, and the amplitude and frequency of EMR signals. Experimental observations from point load and hydraulic fracturing tests were conducted to investigate the variation patterns of EMR signals within the frequency range of 100 Hz–100 kHz during the evolution of single and multiple cracks in concrete. The experimental results indicate that the amplitude of the EMR signals generated during concrete cracking is directly proportional to the applied stress while inversely proportional to the square of the observation distance. The proposed signal generation and emission mechanism is in strong agreement with the observed experimental phenomena. This theoretical framework provides a deeper understanding of the intrinsic causes of EMR signal generation during concrete cracking, enhancing the interpretability and applicability of EMR-based monitoring techniques for concrete cracking.

Health status monitoring of power electronic devices is essential for ensuring their safe and reliable operation. In recent years, monitoring switching transient electromagnetic voiceprint (EMVP) signals has emerged as a promising new indicator for assessing device health. However, the identification of aging states based on EMVP still relies on manual interpretation, leading to low accuracy. This paper presents a health status monitoring method for Insulated Gate Bipolar Transistor (IGBT) devices using EMVP signals, applicable across various operating conditions. Specifically, we propose a spatiotemporal feature fusion cross attention neural network for aging state identification. Experimental results demonstrate that the network achieves an accuracy of over 95% in detecting the aging state of IGBT devices. Furthermore, a transfer learning approach is introduced to improve the model’s effectiveness and generalization ability when working with small sample datasets. The proposed monitoring method facilitates accurate and rapid evaluation of IGBT device aging states under diverse operational conditions.