Chao Xu

1. Bolted joints are widely used in civil, aerospace and mechanical structures, where their tightening condition directly influences structural safety and service reliability [1–3]. During the serv...

Structural health monitoring (SHM) is critical for ensuring the long-term, high-reliability service of aircraft structures and systems. Accurate damage localization is a key aspect of SHM technique development. Guided wave (GW) and electromechanical impedance (EMI) are two widely used SHM techniques. GW is effective for large-area damage detection, while EMI offers high damage monitoring sensitivity near sensors. However, both traditional GW and EMI methods suffer from regions of reduced damage monitoring sensitivity, which may result in regions of low detectability and undermine reliable damage detection. Missed detections may allow minor damage to grow rapidly, potentially leading to catastrophic structural failure. To address this issue, this paper proposes a subregion fusion strategy that leverages the complementary strengths of GW and EMI. The monitoring area is divided into near and far subregions based on the damage monitoring sensitivity range of the EMI damage index. In the near subregion, GW and EMI probabilistic imaging results are fused to eliminate regions of low detectability. In the far subregion, GW probabilistic imaging is enhanced using a distribution coefficient derived from the EMI damage index. Experiments on aluminum and composite plates show that the newly proposed fusion method reduces average localization errors by 32% and 39% compared to traditional GW probabilistic imaging, and by 53% and 54% compared to EMI probabilistic imaging, respectively. These results demonstrate that the proposed fusion strategy effectively eliminates regions of low detectability and improves damage localization accuracy across the entire monitoring area. With its strong extendibility and potential for continuous improvement, the proposed subregion fusion imaging method is expected to serve as a valuable tool for long-term structural integrity assurance in complex and mission-critical systems, including thin-walled aerospace components such as reusable spacecraft, rocket fuel tanks, and engine shells, as well as thin-walled civil and industrial structures such as pressure vessels, large oil and chemical storage tanks, and thin-walled steel structural elements.

Traditional ultrasonic bolt stress measurement is hindered by high power consumption. Lowering excitation voltage reduces power but degrades signal-to-noise ratio (SNR), compromising accuracy. This paper proposes a synergistic algorithm combining Empirical Mode Decomposition (EMD) with Adaptive Threshold Wavelet Denoising (ATWD). The method preserves transient features by reconstructing high-frequency components via EMD, then suppresses noise by precisely processing low-frequency components using ATWD. Finally, cross-correlation estimates ultrasonic delay. Evaluated at excitation voltages from 12 V to 0.5 V, the EMD-ATWD method maintains measurement errors below 10% even at 0.5 V, improving accuracy by over 48% compared to conventional Finite Impulse Response (FIR) and Threshold Wavelet Denoising (WTD) methods, while enhancing key echo waveform fidelity by over 35%. This method provides a reliable low-power bolt stress monitoring idea for engineering applications.

Bolted joint structures are critical fastening components across various engineering applications, and the ability to monitor their contact status is crucial for effective structural health monitoring (SHM). The acoustic emission (AE) technique combined with deep learning (DL) methods has been extensively applied in bolt looseness monitoring. Current DL methods assume that the data distribution remains consistent between training and testing datasets. In fact, the surface contact state and the resulting AE signal will be different after each assembly. To address the domain shifts caused by variations in surface contact states and AE signal characteristics across different assemblies, this paper presents a domain‐generalized framework using acoustic emission (DGFAE) for bolt looseness diagnosis without requiring prior access to target domain data. The framework integrates a compound loss function capturing the ordinal progression of bolt loosening and employs deep correlation alignment (Deep CORAL) to enhance feature alignment across domains. The effectiveness of the DGFAE method is validated using the “ORION‐AE” dataset, with ablation experiments and comparative analysis against other domain generalization (DG) techniques. Compared to state‐of‐the‐art DG methods, superior diagnostic accuracy is achieved under unseen target conditions. Furthermore, a pseudo‐ DG scenario is explored, where partial healthy samples from the target domain are assumed to be accessible, and the Mixup augmentation technique is integrated to further improve generalization robustness. The diagnostic results confirm that the proposed DGFAE method provides a practical and effective solution for bolt looseness monitoring in real‐world engineering settings.

1. With advancements in aerospace engineering, membrane-based structures are in high demand for the development of large-scale spacecraft components such as flexible solar arrays (Yan et al. 2025),...

Radially bolted cylindrical–cylindrical shell joints are critical load-bearing components in aerospace vehicles. These joints experience complex thermo–mechanical environments during flight, where aerodynamic heating and mechanical loads jointly induce nonlinear deformation and stiffness variation through evolving interfacial contact states. To elucidate these mechanisms, this study develops a sequentially coupled thermo–mechanical finite-element framework to analyze the stiffness evolution of RBCCSJs under transient heating and combined mechanical loads (tension, compression, and bending). The results show that the global stiffness evolves through distinct contact-controlled stages (sticking → microslip → macroslip → mechanical bearing), producing pronounced nonlinear stiffness troughs spanning over two orders of magnitude. Under tension and bending, stiffness peaks during full sticking and decreases with slip, whereas under compression, it recovers earlier due to its end-face-bearing formation. Transient heating introduces two competing effects, thermal-expansion-induced frictional stiffening during short-term heating and temperature-dependent material softening during sustained exposure, leading to a 19.2–34% reduction in stiffness under steady thermal conditions. These findings clarify the dominant role of contact-state evolution and thermo–mechanical coupling in joint behavior and provide a quantitative analytical basis for enhancing the stiffness reliability and design optimization of aerospace bolted assemblies operating in transient thermal environments.