Uncertainty Evaluation in the Design of Structural Health Monitoring Systems for Damage Detection†

Kabban, Christine Schubert; Uber, Richard; Lin, Kevin K.; Lin, Bin; Bhuiyan, Yeasin; Giurgiutiu, Victor

doi:10.3390/aerospace5020045

Cited by 7 publications

(4 citation statements)

References 28 publications

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“…[10][11][12][13] and its structural health states (impact, crack, etc.). [14][15][16][17][18][19][20] Therein, structural health monitoring (SHM) is one of the key technologies of flexible smart skin, which plays a crucial role in structural response analysis, 21 fatigue monitoring, 22 damage prevention, 23 and other safety issues. Furthermore, the synchronous monitoring of structural health status and aerodynamic characteristics has gradually become the research highlights, 24 which puts forward new requirements and challenges for ultra-thin designing and large-area monitoring of SHM technology.…”

Section: Introductionmentioning

confidence: 99%

Flexible, monolithic piezoelectric sensors for large-area structural impact monitoring via MUSIC-assisted machine learning

Zhu

Hou

et al. 2023

Structural Health Monitoring

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Aircraft smart skin requires the integration of large-scale, ultrathin, and high-sensitive sensor network on the surface for structural health monitoring (SHM). However, it is fairly difficult to fabricate such large-area flexible sensor networks with low cost and high reliability. Although flexible and monolithic sensors are easy to be fabricated, their applications in large-area impact monitoring have yet to be developed and revealed. In this study, an impact monitoring and localization technology based on monolithic small-area sensors is proposed for large-area structures. The pivotal principle lies in the combination of the flexible piezoelectric sensor array and the Multiple Signal Classification (MUSIC)-assisted Artificial Intelligence Network (ANN) algorithm, where the key sorted feature matrix from the output signals to ANN can be captured by the MUSIC algorithm to achieve the spatial location estimation. The results show that the flexible piezoelectric sensors demonstrate excellent performance to monitor structural impacts in a region of more than 7500% of its area. Secondly, excellent conformation between the sensors and complex surfaces is achieved by the ultrathin thickness almost without affecting the surficial flow field. The overall recognition accuracy and robustness of impact location of machine learning is greatly increased by the integration of MUSIC algorithm, which is immune to the shape, material, thickness, or opening holes of the structure. Except for the impact location, impact energy, impact frequency, impact hardness and other structural health parameters can also be monitored. Therefore, a great prospect in the integrated monitoring of the aircraft’s structural and environmental parameters is expected.

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Section: Introductionmentioning

confidence: 99%

Flexible, monolithic piezoelectric sensors for large-area structural impact monitoring via MUSIC-assisted machine learning

Zhu

Hou

et al. 2023

Structural Health Monitoring

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“…Memmolo et al [ 17 ] determined the POD based on a scalar damage metric and numerical simulations. More recently, Schubert Kabban et al [ 18 ] described how to reduce the experimental effort by combining efficient statistical design and numerical simulations. In addition, Tschöke and Gravenkamp [ 19 ] worked on advanced fast simulation methods that can support a model-based POD.…”

Section: Introductionmentioning

confidence: 99%

Performance Assessment for a Guided Wave-Based SHM System Applied to a Stiffened Composite Structure

Mueller

Memmolo

Tschöke

et al. 2022

Sensors

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To assess the ability of structural health monitoring (SHM) systems, a variety of prerequisites and contributing factors have to be taken into account. Within this publication, this variety is analyzed for actively introduced guided wave-based SHM systems. For these systems, it is not possible to analyze their performance without taking into account their structure and their applied system parameters. Therefore, interdependencies of performance assessment are displayed in an SHM pyramid based on the structure and its monitoring requirements. Factors influencing the quality, capability and reliability of the monitoring system are given and put into relation with state-of-the-art performance analysis in a non-destructive evaluation. While some aspects are similar and can be treated in similar ways, others, such as location, environmental condition and structural dependency, demand novel solutions. Using an open-access data set from the Open Guided Waves platform, a detailed method description and analysis of path-based performance assessment is presented.The adopted approach clearly begs the question about the decision framework, as the threshold affects the reliability of the system. In addition, the findings show the effect of the propagation path according to the damage position. Indeed, the distance of damage directly affects the system performance. Otherwise, the propagation direction does not alter the potentiality of the detection approach despite the anisotropy of composites. Nonetheless, the finite waveguide makes it necessary to look at the whole paths, as singular phenomena associated with the reflections may appear. Numerical investigation helps to clarify the centrality of wave mechanics and the necessity to take sensor position into account as an influencing factor. Starting from the findings achieved, all the issues are discussed, and potential future steps are outlined.

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“…Many researchers examined the effectiveness of systems by using various methods, including simulations, analytical calculations, computational workflows, and various factors-based and model-assisted approaches, with the goal of validating the systems' economic advantages in practice. Kabban et al (2018) proposed a simulation model by using a time-and cost-efficient method to determine sensitive factors of a structural health monitoring (SHM) system in order to enable SHM validation [17]. Madni et al (2019) proposed a methodological framework for analyzing investments in and potential gains from satellites, considering system complexity, environmental complexity and regulatory constraints, and system lifespan [18].…”

Section: Introductionmentioning

confidence: 99%

“…Li et al (2018) focused on the assessment of the coverage effectiveness of remote sensing satellites and proposed a multi-index evaluation method based on index weight using an entropy weight method and AHP [24]. In the mentioned papers [17][18][19][20][21][22][23][24], commercial profit is not within the index system, which means the authors cannot measure the economic value of the task scheduling for satellites. Additionally, the effects of weather uncertainties on different observation targets were not considered.…”

Section: Introductionmentioning

confidence: 99%

An Effectiveness Evaluation Model for Satellite Observation and Data-Downlink Scheduling Considering Weather Uncertainties

et al. 2019

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Low Earth orbit (LEO) satellites play an important role in human space activities, and market demands for commercial uses of LEO satellites have been increasing rapidly in recent years. LEO satellites mainly consist of Earth observation satellites (EOSs), the major commercial applications of which are various sorts of Earth observations, such as map making, crop growth assessment, and disaster surveillance. However, the success rates of observation tasks are influenced considerably by uncertainties in local weather conditions, inadequate sunlight, observation dip angle, and other practical factors. The available time windows (ATWs) suitable for observing given types of targets and for transmitting data back to ground receiver stations are relatively narrow. In order to utilize limited satellite resources efficiently and maximize their commercial benefits, it is necessary to evaluate the overall effectiveness of satellites and planned tasks considering various factors. In this paper, we propose a method for determining the ATWs considering the influence of sunlight angle, elevation angle, and the type of sensor equipped on the satellite. After that, we develop a satellite effectiveness evaluation (SEE) model for satellite observation and data-downlink scheduling (SODS) based on the Availability–Capacity–Profitability (ACP) framework, which is designed to evaluate the overall performance of satellites from the perspective of time resource utilization, the success rate of tasks, and profit return. The effects of weather uncertainties on the tasks’ success are considered in the SEE model, and the model can be applied to support the decision-makers on optimizing and improving task arrangements for EOSs. Finally, a case study is presented to demonstrate the effectiveness of the proposed method and verify the ACP-based SEE model. The obtained ATWs by the proposed method are compared with those by the Systems Tool Kit (STK), and the correctness of the method is thus validated.

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Uncertainty Evaluation in the Design of Structural Health Monitoring Systems for Damage Detection†

Cited by 7 publications

References 28 publications

Flexible, monolithic piezoelectric sensors for large-area structural impact monitoring via MUSIC-assisted machine learning

Flexible, monolithic piezoelectric sensors for large-area structural impact monitoring via MUSIC-assisted machine learning

Performance Assessment for a Guided Wave-Based SHM System Applied to a Stiffened Composite Structure

An Effectiveness Evaluation Model for Satellite Observation and Data-Downlink Scheduling Considering Weather Uncertainties

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