An energy efficient wireless module for on-board aircraft impact detection

Growing incidents of structural damage and failures underscore the urgent need for more advanced Structural Health Monitoring (SHM) solutions. While Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) has revolutionised SHM by enabling automated, long-term, and large-scale displacement monitoring of structures using Persistent Scatterers (PSs), its applicability is often constrained by the unpredictable spatial distribution of PSs. Conventional suitability assessments that rely primarily on PS density fail to account for the underlying structural behaviours, limiting their reliability. This paper introduces a novel structural-based inverse approach that uniquely integrates MT-InSAR characteristics with structural response modelling to overcome these limitations. Unlike existing approaches, the method explicitly evaluates whether observed surface displacements adequately represent a target damage mechanism by comparing outputs from a pseudo sensor with those from a virtual MT-InSAR sensor. If this condition is satisfied, it then determines the minimum required number and optimal spatial arrangement of ideal PSs using modified pivoted QR factorisation, where satellite-induced positional uncertainties are rigorously modelled through Radial Basis Function kernels. The proposed method was validated on a quay wall in Amsterdam using Finite Element Method (FEM) simulations of three distinct damage mechanisms. Results demonstrate its unique capability to quantitatively assess displacement representativeness and to pinpoint ideal PSs for robust monitoring. Leveraging these insights, the method was further applied to evaluate MT-InSAR monitoring feasibility across Amsterdam’s historic centre, successfully identifying quay wall segments amenable to reliable observation. This work represents a significant advancement in MT-InSAR-based SHM, providing a more targeted and structurally informed approach for real-world infrastructure monitoring. • Developed a structural-based inverse approach to evaluate MT-InSAR for SHM. • Integrates structural information with MT-InSAR position uncertainties. • Overcomes limitations of PS-density methods via structure-informed evaluation. • Scalable for large-region SHM, demonstrated using MT-InSAR data from Amsterdam. • Enables intelligent SHM by linking PS distributions to structural mechanisms.

Fiber optical sensors (FOS) have been widely used to ensure physical parameter monitoring such as strain, temperature, vibration, etc. Fiber Bragg grating (FBG) sensors are of interest mainly as they offer relatively easy integration, multiplexing capabilities, and other advantages. A topical area of investigation for FOS integration is structural health monitoring (SHM) and perimeter-security monitoring. Different objects and infrastructures have certain needs, such as long-distance (40+ km) placement and monitoring solutions, lack of active power supply availability on the site, etc. It is vital to study and develop specific FBG profiles to ensure optimal operation of FBG in security, perimeter, and SHM solutions. In this research, we have evaluated the areas and categories in which such FOS and FBGs, in particular, have been used regarding SHM and perimeter monitoring solutions. Authors have also studied, simulated, and experimentally tested various FBGs that can be implemented for the perimeter—security applications and in SHM solutions.

Vibration-based damage detection approaches have received considerable attention in the field of structural health monitoring. Modal parameters are often adopted to define damage-sensitive features, since their strong physical meaning can help interpreting the structural condition. On the other hand, they are also sensitive to changes to environmental and operational conditions. This aspect is critical for an automatic damage detection, because changes in modal parameters due to varying external factors (e.g. temperature) can be greater than those caused by damage.In this context, this paper proposes an application to real data coming from long-term structural health monitoring of axially-loaded beam-like structures under realistic environment. These very common structural elements are usually subject to an axial load that changes under environmental and operating conditions. Since the axial load is not directly known in most real applications, assessing damage using modal-based damage features is a complicated task.In this paper, a data driven approach that does not require a knowledge of the axial load is proposed to filter out the environmental effects on modal-based damage features. The strategy has been successfully tested on data acquired in an uncontrolled environment, and resulted in being a promising solution for real structural health monitoring applications.KeywordsLong-term monitoringDamage detectionEnvironmental and operational variationsVibration-based featureStatistical pattern recognitionBeam-like structureTie-rod

<ns3:p>Assess the thermomechanical performance of pultruded glass-fibre-reinforced polymer (GFRP) rods containing an embedded distributed fibre-optic sensor (DFOS) for structural health monitoring (SHM). Quantify the impact of integrating the sensor on the curing behaviour, thermal stability and mechanical properties, and compare this with nominally identical rods without a sensor.Two rod variants were pultruded: one containing a sensor (OS-GFRP) and one without (No-OS-GFRP). The epoxy matrix was tuned using rheology, and the DFOS was embedded on axis without a protective coating. Differential scanning calorimetry (DSC) was employed to analyse the glass transition, residual curing, and degradation onset. It was followed by quasi-static tests (tensile, three-point bending and short-beam shear), while Charpy impact tests were used to quantify energy absorption. Microscopic studies (optical and SEM-EDS) examined the quality of the interphase and the presence of defects.The integration of embedded Distributed Fibre Optic Sensing (DFOS) was achieved without materially compromising the composite’s structural integrity or load-bearing capacity. Thermally, DFOS did not systematically reduce the glass transition temperature or thermal stability. Mechanically, while the axial modulus decreased by approximately 6% and shear properties were modestly lower, the mean tensile strength was maintained, flexural strength increased by ~8%, and impact energy absorption rose by 9% with less scatter. Microscopy revealed that minor local interphase perturbations, such as micro-voids, were consistent with the small penalties to shear properties. Overall, the presence of DFOS enables sensing functionality, with any compromises being minor and predictable, and process tuning can effectively mitigate manufacturing inconsistencies.The observed differences, particularly with regard to shear and scatter, suggest that the interphase is sensitive to both curing kinetics and local defects. Therefore, future work should focus on optimising the curing process around the optical fibre, refining the interphase chemistry, and investigating fatigue, creep, and durability in alkaline and concrete environments.Pultruded GFRP rods with on-axis distributed fibre-optic sensors (DFOS) are a robust, easy-to-install solution for structural health monitoring (SHM) of concrete and civil infrastructure. They provide distributed strain and temperature measurements with minimal impact on structural performance, enabling data-driven maintenance and safety.Demonstrates a production-feasible route to integrate DFOS during pultrusion, links DSC-diagnosed local curing phenomena to small but interpretable mechanical deltas, and evidences that embedded sensing can coexist with near-baseline thermomechanical properties in GFRP rods.</ns3:p>

With the advancement of aerospace technology, the importance of Structural Health Monitoring (SHM) in ensuring the safety of critical equipment has become increasingly evident. Nickel-based superalloys are widely utilized in key components such as turbine blades and turbine disks due to their exceptional high-temperature performance. Accurate measurement of residual stress is crucial for enhancing material reliability and extending the service life of components, particularly in single-crystal nickel-based superalloys where measurement becomes complex due to anisotropy. In this study, a novel method based on a particle swarm optimization algorithm is proposed to improve the precision of crystal orientation measurement and pole figure calibration. This method establishes a local extremum filtering technique and a standard pole figure model, thereby optimizing the measurement process and addressing the limitations of traditional methods when dealing with high-index crystal planes. Experimental validation is conducted on single-crystal turbine blades subjected to different shot peening intensities, demonstrating significant improvements in measurement efficiency and accuracy. This approach provides a new solution for structural health monitoring in the aerospace field.

This paper proposes a hybrid structural health monitoring (SHM) solution for a smart composite patch repair for aircraft structures based on piezoelectric (PZT) and fibre optic (FO) sensors to monitor the integrity of a the bondline and detect any degradation. FO sensors are used to acquire guided waves excited by PZT transducers to allow the advantages of both sensor technologies to be utilised. One of the main challenges of guided wave based detection methodologies is to distinguish the effect of temperature on the propagating waves, from that of an existing damage. In this research, the application of the hybrid SHM system is tested on a composite step sanded repair coupon under operational condition (temperature variation) representative of an aircraft for the first time. The sensitivity of the embedded FO sensor in recording the strain waves is compared to the signals acquired by PZT sensors under varying temperature. A novel compensation algorithm is proposed to correct for the effect of the temperature on the embedded FO sensor spectrum in the hybrid set-up. The repaired specimen is then impacted with a drop mass to cause barely visible impact damage (BVID). The hybrid SHM system is then used to detect the damage, and its diagnosis results are compared to a PZT only based smart repair solution. The results show promising application of the hybrid solution for monitoring bondline integrity as well as highlighting challenges of the embedding of FO sensors for a reliable and repeatable diagnosis.

Implementing Structural Health Monitoring (SHM) is crucial for safeguarding the integrity and extending the lifespan of buildings and infrastructure. Therefore, continuous structural vibration monitoring for early detection of damage and timely prevention of failures has become an urgent topic for developing monitoring methods using the Internet of Things (IoT), microelectromechanical systems (MEMS), and low-cost wireless sensor networks. The MPU6050, a widely used 6-axis Inertial Measurement Unit (IMU) combining an accelerometer and gyroscope, offers an affordable and efficient solution for vibration sensing in SHM applications. When integrated into an IoT system, this sensor can transmit real-time vibration data through various communication methods-wired connections, Wi-Fi, or electromagnetic (EM) wave-based wireless links-to assess structural integrity remotely. This paper approaches the an IoT-based SHM system using the MPU6050 accelerometer to monitor building vibrations. The research evaluates data transmission approaches (wired, EM wave, and Wi-Fi) regarding accuracy and reliability. The approach of a 4-degree-of-freedom (4-DOF) building model with known dynamic parameters helps verify the reliability of the three methods. In addition, the vibration measurements of a real building in 2 cases, before and after the earthquake, also show promising results for the application of this study for real-world SHM deployment, balancing performance with cost and scalability.

Fundamental shear horizontal (SH0) waves in plate-like structures are of great importance in structural health monitoring (SHM) applications due to its unique nondispersive nature. The generation and the reception of the SH0 waves using piezoelectrics, however, is always a challenge. In this study, a dynamic model on the shear horizontal (SH) wave generation is proposed based on the continuum mechanics theory, in which the full dynamic coupling between a PZT actuator and a plate is considered. The inevitable unideal bonding between the actuator and the plate is taken into account through a spring model. The solutions, which can simultaneously satisfy the governing dynamic equations and the boundary conditions, are obtained in a closed form using trigonometric series and modal superposition method. The convergence of the series is demonstrated and the model is numerically validated through comparisons with FEM results using a PZT-5H wafer bonded on the surface of a thin aluminum plate. The proposed model is general, suitable for both ideal and unideal bonding, and expected to provide a useful tool for analyzing and simulating SH0 wave generations in SHM applications.

Acoustic emission characteristics of stainless steel reinforced geopolymer coral concrete beams under four-point bending

Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

Applications of Machine Learning (ML) algorithms in Structural Health Monitoring (SHM) have become of great interest in recent years owing to their superior ability to detect damage and deficiencies in civil engineering structures. With the advent of the Internet of Things, big data and the colossal and complex backlog of aging civil infrastructure assets, such applications will increase very rapidly. ML can efficiently perform several analyses of clustering, regression and classification of damage in diverse structures, including bridges, buildings, dams, tunnels, wind turbines, etc. In this systematic review, the diverse ML algorithms used in this domain have been classified into two major subfields: vibration-based SHM and image-based SHM. The efficacy of deploying ML algorithms in SHM has been discussed and detailed critical analysis of ML applications in SHM has been provided. Accordingly, practical recommendations have been made and current knowledge gaps and future research needs have been outlined.

Structural health monitoring has arisen as an important tool for managing and maintaining civil infrastructure. A critical problem for all structural health monitoring systems is data loss or data corruption due to sensor failure or other malfunctions, which bring into question in subsequent structural health monitoring data analysis and decision-making. Probability density functions play a very important role in many applications for structural health monitoring. This article focuses on data loss compensation for probability density function estimation in structural health monitoring using imputation methods. Different from common data, continuous probability density functions belong to functional data; the conventional distribution-to-distribution regression technique has significant potential in missing probability density function imputation; however, extrapolation and directly borrowing shape information from the covariate probability density function are the main challenges. Inspired by the warping transformation of distributions in the field of functional data analysis, a new distribution regression approach for imputing missing correlated probability density functions is proposed in this article. The warping transformation for distributions is a mapping operation used to transform one probability density function to another by deforming the original probability density function with a warping function. The shape mapping between probability density functions can be characterized well by warping functions. Given a covariate probability density function, the warping function is first estimated by a kernel regression model; then, the estimated warping function is used to transform the covariate probability density function and obtain an imputation for the missing probability density function. To address issues with poor performance when the covariate probability density function is contaminated, a hybrid approach is proposed that fuses the imputations obtained by the warping transformation approach with the conventional distribution-to-distribution regression approach. Experiments based on field monitoring data are conducted to evaluate the performance of the proposed approach. The corresponding results indicate that the proposed approach can outperform the conventional method, especially in extrapolation. The proposed approach shows good potential to provide more reliable estimation of distributions of missing structural health monitoring data.

Probability of detection (POD) calculations in structural health monitoring (SHM) applications are complicated by the dependency of measurements obtained on the same structure, among other factors. This article presents a repeated measures method to extend POD signal-response modeling to correctly describe a population of repeated measurements while estimating the variance due to dependence in the observations. In particular, equations are presented which develop an autoregressive correlation structure to model continuous observations that are correlated in time. Software implementation of these models is discussed and methodology to simulate correlated datasets is presented. The combination of these tools enables a method of POD estimation in SHM applications through the appropriate mathematical extensions of the statistical modeling.

The design, development, and testing of a low-cost phase optical time-domain reflectometry (Phase-OTDR) system, intended for use in structural health monitoring (SHM) applications, are presented. Phase-OTDR is a technology that is growing and evolving at an impressive rate. Systems based on this principle are becoming very sensitive and elaborate and can perform very accurate condition monitoring, but at the same time, they are critically alignment-dependent and prohibitively costly to be considered as viable options in real field applications. Certain Phase-OTDR systems have been applied in real field studies, but these examples are mostly a proof-of-concept. The system presented here is the result of a compromise between performance and cost, using commercial components, specifically combined and tuned for SHM applications. The design and implementation of all the electronic and optoelectronic steps are presented, and the operation of the system is demonstrated, achieving a spatial resolution of ~6 m over 5 km. This work provides useful engineering guidelines for the low-cost implementation of Phase-OTDR systems. It is anticipated that the affordable development of such interrogation systems will promote their use in a wide range of SHM applications with moderate monitoring requirements and will assist the penetration of Phase-OTDR technology in the industry.

The active sensing methods using piezoelectric materials have been extensively investigated for the efficient use in structural health monitoring (SHM) applications. Relying on high frequency structural excitations, the methods showed the extreme sensitivity to minor defects in a structure. Recently, a sensor self-diagnostic procedure that performs in situ monitoring of the operational status of piezoelectric (PZT) active sensors and actuators in SHM applications has been proposed. In this investigation, previously developed impedance models were revisited in order to investigate the effects of sensor and/or bonding defects on the admittance measurement. New parameters for sensor quality assessment of a PZT and coupling degradation effects between a PZT and bonding layer were incorporated into the traditional electromechanical impedance model for better estimation of the electromechanical impedance signatures and sensor diagnostics. The feasibility of the modified impedance model for sensor self-diagnosis using the admittance measurements was demonstrated by a series of parametric studies using a simple example of PZT-driven single degree of freedom spring-mass-damper system. This paper summarizes the description of the proposed modified electromechanical impedance model, parametric studies for impedance-based sensor diagnostics, and several issues that can be used as a guideline for future investigation.