Quantitatively and accurately monitoring the damage to composites is critical for estimating the remaining life of structures and determining whether maintenance is essential. This paper proposed an active sensing method for damage localization and quantification in composite plates. The probabilistic imaging algorithm and the statistical method were introduced to reduce the impact of composite anisotropy on the accuracy of damage detection. The matching pursuit decomposition (MPD) algorithm was utilized to extract the precise TOF for damage detection. The damage localization was realized by comprehensively evaluating the damage probability evaluation results of all sensing paths in the monitoring area. Meanwhile, the scattering source was recognized on the elliptical trajectory obtained through the TOF of each sensing path to estimate the damage size. Damage size was characterized by the Gaussian kernel probability density distribution of scattering sources. The algorithm was validated by through-thickness hole damages of various locations and sizes in composite plates. The experimental results demonstrated that the localization and quantification absolute error are within 11 mm and 2.2 mm, respectively, with a sensor spacing of 100 mm. The algorithm proposed in this paper can accurately locate and quantify damage in composite plate-like structures.

Aerogels, known for their lightweight, porous structure and exceptional thermal insulation properties, have attracted increasing attention across research and engineering domains. Recent advancements in material composition and structural design have led to the development of flexible aerogels, broadening their applications across diverse fields. This review provides a comprehensive overview of the fabrication methods for flexible thermal insulation aerogels, including freeze-drying, phase separation, 3D printing, and fiber formation. Their multifunctional applications in aerospace, construction, and battery industry are also highlighted. Recent progress has demonstrated their applicability as lightweight insulation for spacecraft, energy-efficient building materials, and thermal management layers in advanced batteries, underscoring their versatility in both traditional and emerging technologies. Finally, the challenges and future development for flexible thermal insulation aerogels are discussed, offering valuable insights for advancing this promising material.

Bolted joints play important roles in ensuring the integrity of composite structures. Information obtained through structural health monitoring (SHM) can be used to determine the health condition of composite bolted joints. However, integrating monitoring sensors with the composite bolted joints is an extremely challenging problem. In this study, a nanocomposite piezoresistive sensor made of carbon black (CB) and polyvinyl pyrrolidone (PVP) is applied to the composite bolted joints looseness monitoring. The change in the mass fraction of CB particles leads to the different resistance values, which are measured to analyze the conductive property of the sensor. The mechanical properties of the sensor are demonstrated under pressure loading with different mass fractions of CB particles. The nanocomposite piezoresistive sensor is integrated with the carbon fiber reinforced plastics (CFRP) single-lap bolted joints, and the optimized electrode layout is presented by comparing the change of resistance and the sensitivity. The state of CFRP bolted joints looseness is represented by the change of relative resistance with different torques. The experimental results show that the CB/PVP nanocomposite piezoresistive sensor has great potential in SHM to realize the integration of the sensor and life-cycle monitoring of composite bolted joints.

Wind energy is a widely utilized renewable energy source. Enhancing the reliability of wind turbines (WTs), especially the gearboxes, is crucial for efficient power generation. Real-time structural health monitoring (SHM) of the gearbox, including pre-start self-testing and ongoing checks, is essential for achieving optimal performance and safety. In this study, we developed a laboratory gear transmission platform simulating the WT spindle gearbox and integrated it with Ultrasonic Guided Wave (UGW) technology for active monitoring. Experiments were conducted to monitor the gearbox in three health states under five different working conditions. A modified health indicator (HI), MWF-PeEn, was extracted from UGW signals. However, significant spatial mapping variations of the HI matrix across different conditions hindered diagnostic accuracy. To address this issue, transfer learning techniques were applied to optimize the Extreme Learning Machine (ELM), improving the spatial distribution consistency of HI, and enhancing diagnostic accuracy. The precision and recall rate of the proposed FAA-ELM model are all above 90%. Compared to recent models, the FAA-ELM showed nearly a 10% improvement in precision, demonstrating its effectiveness and robustness in real-world scenarios. This underscores the practical advantages of SHM and the FAA-ELM for reliable WT maintenance and operation.

Carbon fiber reinforced plastics (CFRP) have been extensively utilized in large composite structures and various advanced engineering applications. Consequently, the bolted joint structure is also applied to connect large composite structures, which must be monitored in real time to ensure structural safety and integrity. In an effort to enhance integration and monitoring sensitivity, this study investigates a novel method utilizing a carbon-based nanocomposite piezoresistive sensor network. The proposed sensor network comprises carbon black (CB) and carbon nanotubes (CNT) as conductive fillers embedded within a polyvinylpyrrolidone (PVP) substrate. The conductive and mechanical properties of the CB/CNT piezoresistive sensor are extensively investigated in the study. Moreover, the inclusion of sodium dodecyl benzene sulfonate (SDBS) as a surfactant in the sensor aims to enhance the dispersion of the conductive fillers. Moreover, a sensor layout strategy for monitoring multiple failure modes of CFRP bolted joint structure has been demonstrated. The results show that different failure modes can be characterized by the resistance signal change of the sensor. The developed sensor network is specifically engineered to monitor multiple failure modes characteristic of CFRP bolted joints. The outcomes demonstrate the exceptional reliability of the proposed sensor in monitoring various failure modes and underscore its considerable potential in structural health monitoring (SHM) applications.