Structural Health Monitoring is an international peer reviewed journal that publishes the highest quality original research that contain theoretical, analytical, and experimental investigations that advance the body of knowledge and its application in the discipline of structural health monitoring.

Structural Health Monitoring is an international peer-reviewed journal dedicated to publishing high-quality original research. This research includes theoretical, analytical, and experimental investigations aimed at advancing the body of knowledge and its applications in the field of structural health monitoring.

《Structural Health Monitoring》是一本国际同行评审期刊，致力于发表高质量的原创研究。这些研究包括理论、分析和实验调查，旨在推动结构健康监测领域的知识体系及其应用的发展。

Structural Health Monitoring

A fatigue crack and its precursor often serves as a source of nonlinear mechanism for ultrasonic waves, and nonlinear ultrasonic techniques have been widely studied to detect fatigue crack at its very early stage. In this study, a wireless sensor node based on nonlinear ultrasonics is developed specifically for fatigue crack detection: (1) through packaged piezoelectric transducers, ultrasonic waves at two distinctive frequencies are generated, and their modulation due to a microcrack (less than 0.1 mm in width) is detected; (2) an autonomous reference-free crack detection algorithm is developed and embedded into the sensor node, so that users can simply “stick” the sensor to a target structure and automatically “detect” a fatigue crack without relying on any history data of the target structure; and (3) the whole design of the sensor node is fulfilled in a low-power working strategy. The performance of the sensor node is experimentally validated using aluminum plates with real fatigue cracks and compared with that of a conventional wired system. Furthermore, a field test in Yeongjong Grand Bridge in South Korea has been conducted with the developed sensor nodes.

Peipei Liu

Hyung Jin Lim

Suyoung Yang

Hoon Sohn

Cheul Hee Lee

Yung Yi

Daewoo Kim

Jinhwan Jung

In-hwan Bae

Development of a “stick-and-detect” wireless sensor node for fatigue crack detection

Through the integration of advanced sensors, actuators, and micro-processors, aircraft smart skin technology can improve the structural performance of aircraft and make them self-perception, self-diagnosis, self-adaptation, self-learning, and self-repair. Aircraft smart skin for structural health monitoring (SHM) is an important type of aircraft smart skin and has received extensive attention in recent years. Large-scale, lightweight, and low-power consumption are three key problems hindering the realization and engineering applications of aircraft smart skin for SHM. In view of these problems and restrictions of practical aircraft onboard applications, this article reviews the current research progress on aircraft smart skin for SHM, introduces their design, materials, manufacturing process, and monitoring principles in detail, and discusses how they study above problems from these aspects. Finally, perspectives are proposed on the opportunities and future developments of aircraft smart skin for SHM.

Yu Wang

Shuguang Hu

Tao Xiong

Yongan Huang

Lei Qiu

Recent progress in aircraft smart skin for structural health monitoring

飞机智能蒙皮结构健康监测的最新进展

The authors focus on the construction of a structural health monitoring (SHM) system with an advanced grid structure (AGS) made of carbon-fiber reinforced plastic (CFRP). AGS is often applied to aerospace structures because the ribs carry only axial forces in the carbon fiber direction, making AGS structurally effective and lightweight, and because the repetition of many ribs in the AGS composition results in damage tolerance. The failure of a single rib hardly affects the fracture of the whole structure, making AGS a fail-safe structure. In this research, the authors have embedded multiplexed-fiber Bragg grating (FBG) sensors into an AGS rib in the longitudinal direction to measure mechanical strains of all ribs in order to detect the existence and regions of AGS rib fractures. Monitoring the change in rib-longitudinal strains is the most effective SHM system for AGS. To confirm the proposal, the authors explore the following. First, various damage characteristics under low-velocity impact loading are investigated and it is verified that partial rib cracks are the most typical damage in AGS. An AGS is then fabricated with embedded FBG sensors and verified that the SHM system is able to measure all rib strains. Subsequently, it is analytically determined that the change in longitudinal-rib strains is the most appropriate mechanical feature for damage detection. Moreover, a statistical outlier analysis is introduced into the SHM system for automatic damage detection. Finally, AGS is established with the SHM system and verified experimentally. Results confirm that the existence of damage and its regions in AGS can be detected with the proposed SHM system.

Masataro Amano

Yoji Okabe

Nobuo Takeda

Tsuyoshi Ozaki

Structural Health Monitoring of an Advanced Grid Structure with Embedded Fiber Bragg Grating Sensors

先进网格结构嵌入式光纤布拉格光栅传感器的结构健康监测

This paper proposes a framework for obstacle-avoiding autonomous unmanned aerial vehicle (UAV) systems with a new obstacle avoidance method (OAM) and localization method for autonomous UAVs for structural health monitoring (SHM) in GPS-denied areas. There are high possibilities of obstacles in the planned trajectory of autonomous UAVs used for monitoring purposes. A traditional UAV localization method with an ultrasonic beacon is limited to the scope of the monitoring and vulnerable to both depleted battery and environmental electromagnetic fields. To overcome these critical problems, a deep learning-based OAM with the integration of You Only Look Once version 3 (YOLOv3) and a fiducial marker-based UAV localization method are proposed. These new obstacle avoidance and localization methods are integrated with a real-time damage segmentation method as an autonomous UAV system for SHM. In indoor testing and outdoor tests in a large parking structure, the proposed methods showed superior performances in obstacle avoidance and UAV localization compared to traditional approaches.

Ali Waqas

Dongho Kang

Young-Jin Cha

Deep learning-based obstacle-avoiding autonomous UAVs with fiducial marker-based localization for structural health monitoring

基于深度学习的自主避障无人机与基于基准标记的定位技术在结构健康监测中的应用

In the compound fault diagnosis of gearboxes based on vibration signal, the fault feature information often exists in the complex modulation components of the signal. Using demodulation technology to extract the feature information in the signal is the basis for fault diagnosis. In this article, a modulation information detection technology called harmonic modulation bispectrum (HMB) is proposed after considering the multiple coupling relationship between the frequencies in the modulated signal. HMB can extract effective information from complex modulation signal and reduce noise interference in signal. At the same time, a diagnosis strategy suitable for the gearbox compound fault is developed based on HMB. The third-order Renyi entropy is used to realize the adaptive setting of the parameters in HMB, and the marginal spectrum trends of the HMB are calculated by combining the idea of scale space. In addition, combined with the harmonic spectral kurtosis, the effective information of each frequency band in HMBgram is evaluated quantitatively. The frequency band with the most abundant effective information is located, from which the fault feature information is extracted to realize the fault diagnosis finally. The diagnostic effectiveness of this method has been confirmed through simulation and experimental signals, and the superiority of this method has been demonstrated by comparing with other methods.

Miaorui Yang

Kun Zhang

Yonggang Xu

Aijun Hu

Fengshou Gu

The harmonic modulation bispectrum: a modulated vibration signal analysis method for compound fault diagnosis of gearboxes

谐波调制双谱：一种用于齿轮箱复合故障诊断的调制振动信号分析方法

This article introduces a novel methodology for detecting and classifying anomalies in multiple bridges within a geographical region using satellite-based interferometric synthetic aperture radar displacements and environmental measures. The approach uses subspace alignment to harmonize bridge features, enabling the detection of anomalies based on deviations in one bridge compared to the rest of the population. Simulated and real case studies involving steel railway bridges spanning the Po River in Italy demonstrate the effectiveness of the proposed approach, showcasing its potential for large-scale applications. Moreover, the study explores the transferability of knowledge from simulated data to real-world monitoring scenarios, yielding promising results in classifying real instances using synthetic labels. The proposed approach presents practical benefits for bridge monitoring agencies by providing a cost-effective method for enhancing the resilience and safety of transportation infrastructure.

Said Quqa

Antonio Palermo

Francesco Ubertini

Alessandro Marzani

Regional-scale bridge condition monitoring using InSAR displacements and environmental data

利用InSAR位移和环境数据进行区域尺度桥梁状态监测

In ultrasonic testing, omnidirectional wave propagation presents challenges of nonfocus energy loss and interface interference. Traditional phased array (PA) methods, despite their focusing capabilities, suffer from limitations in energy utilization and target detection accuracy. Frequency diverse array (FDA) technology, initially developed for radar, has demonstrated potential for improved range-angle-dependent ultrasonic beampatterns but still exhibits limitations due to S-shaped coupling. This study proposes an ultrasonic nonlinear frequency diverse array (ultrasonic-NFDA) with nonuniform frequency offsets to address these issues. The ultrasonic-NFDA employs modulated frequency offsets by introducing nonlinear carrying functions and incorporating additional parameters to manipulate beamforming and enhances energy concentration at focal points. Furthermore, the multi-objective differential evolution algorithm is used for parameter optimization. The proposed method decouples the range-angle beampattern and improves the signal-to-interference-plus-noise ratio. Numerical evaluations and simulations demonstrate that ultrasonic-NFDA outperforms the ultrasonic linear FDA and PA series, achieving better peak sidelobe level and half-power beam width. Experimental validations confirm the effectiveness of ultrasonic-NFDA in practical ultrasonic testing scenarios, showing significant improvements in defect detection and imaging clarity. The optimized beampatterns effectively reduce artifacts and enhance the precision of defect localization. The proposed integration of nonlinear frequency offsets and parameter optimization in array testing offers a robust solution for more accurate and efficient ultrasonic inspection methodologies.

Xiaolei Xu

Yanping Zhu

Yiming Wang

Fucai Li

Wensheng Su

Zhigang Xue

Ultrasonic nonlinear frequency diverse array: principle and applications

超声非线性频率多样阵列：原理与应用

Despite the significant advancements in computer-vision-based structural damage recognition enhanced by deep learning techniques, challenges persist with training convergence, recognition stability, and model generalization for multi-type damage with small-scale datasets. To address these issues, few-shot learning has emerged as a promising solution to achieve universal damage segmentation using limited annotated images. This study proposes a novel cycle-consistency-constrained few-shot segmentation framework tailored for multi-type structural damage recognition. A cycle-consistency-constrained prototype learning paradigm is constructed to enhance the adequate utilization of limited pixel-level annotations, which is leveraged by establishing a bidirectional mutual supervision mechanism between support and query sets. Subsequently, a non-parametric similarity-guided optimization module is incorporated into the high-level latent feature space of image embedding. This module induces a similarity-driven contrast learning process for each pixel of feature maps and learns universal prototypes that condense the abstract semantic context of foreground (i.e., multi-type damage) and background. Furthermore, a synthetic loss function, which comprises mutually supervised segmentation dice loss, metric loss, and contrastive loss, is designed to ensure the bidirectional pixel-level segmentation accuracy, intra-class compactness, and inter-class separability of learned prototypes for multi-type damage. A multi-type structural damage dataset, encompassing concrete crack, steel fatigue crack, concrete spalling, and steel corrosion, is collected to validate the efficacy, necessity, and generalizability of the proposed method through a series of comparative studies and ablation experiments. The results indicate that segmentation accuracies for multi-type structural damage significantly surpass that of directly training a conventional segmentation model, performing significant improvements in average mean intersection-over-union (mIoU) and mean pixel accuracy (mPA) by 11.5% and 9.1%, respectively. In addition, the adaptability of the proposed method for one-shot learning, using only one annotated image for a completely new damage type, is also corroborated by notable increases of average mIoU and mPA by 8.1% and 7.7%.

Yunlei Fan

Hui Li

Yuequan Bao

Yang Xu

Cycle-consistency-constrained few-shot learning framework for universal multi-type structural damage segmentation

循环一致性约束的少样本学习框架用于通用多类型结构损伤分割

Health indicators (HIs) are greatly significant in sustaining the stable operation of machines and guiding the implementation of predictable maintenance. Traditional HIs such as kurtosis, negentropy, Gini index manifest some concessions in certain scenarios. In this study, we propose a new geometric partition L-Kurtosis (GLK) indicator for bearing condition monitoring leveraging its splendid fault characterization, and it is proved to be advantageous in perceiving bearing incipient fault occurrence and outlining sufficiently bearing full-life degradation trend through substantial experiments with run-to-failure datasets. Integrated with empirical wavelet transform, a GLKgram is constructed to enable the application of the GLK indicator in bearing incipient fault detection. The experimental results deliver that the GLKgram excels at recognizing the fault repetitive transients and resisting the interfering large random shocks and strong noise, further manifesting the superiority of the GLK indicator in identifying fault features.

Jiaquan Tang

Kai Zheng

Yin Bai

Siguo Wen

Dewei Yang

Likang Zhang

Bin Zhang

A new framework based on geometric partition L-Kurtosis indicator for bearing condition monitoring and incipient fault detection

基于几何分区L-峰度指标的新框架用于轴承状态监测和早期故障检测

Abbreviation
Journal Impact	5.80
Quartiles(Global)	INSTRUMENTS & INSTRUMENTATION(Q1)

ISSN	1475-9217, 1741-3168
h-index	86

Publication Information	Publisher: SAGE Publications Ltd，Publishing cycle: Bimonthly，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2002，Proportion of original research papers: 99.00%，Self Citation Rate:10.50%， Gold OA Rate: 8.33%
Average review cycle	网友分享经验：较慢,6-12周
Average recruitment ratio	网友分享经验：较易

Structural Health Monitoring

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Share Submission Experience

Development of a “stick-and-detect” wireless sensor node for fatigue crack detection

Recent progress in aircraft smart skin for structural health monitoring

Structural Health Monitoring of an Advanced Grid Structure with Embedded Fiber Bragg Grating Sensors

Deep learning-based obstacle-avoiding autonomous UAVs with fiducial marker-based localization for structural health monitoring

The harmonic modulation bispectrum: a modulated vibration signal analysis method for compound fault diagnosis of gearboxes

Regional-scale bridge condition monitoring using InSAR displacements and environmental data

The harmonic modulation bispectrum: a modulated vibration signal analysis method for compound fault diagnosis of gearboxes

Ultrasonic nonlinear frequency diverse array: principle and applications

Cycle-consistency-constrained few-shot learning framework for universal multi-type structural damage segmentation

A new framework based on geometric partition L-Kurtosis indicator for bearing condition monitoring and incipient fault detection