Structural Health Monitoring of Aerospace Composites

Khodaei, Zahra Sharif

doi:10.1016/b978-0-12-822944-6.00046-3

Cited by 2 publications

(2 citation statements)

References 74 publications

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“…This challenge necessitates a deep understanding of the service environment and load-bearing capacity of structures, as well as effective prediction and management of potential damages. To further enhance the confidence in structural integrity assessments, structural health monitoring technologies for real-time monitoring and evaluation of the service state of structures play a significant role in improving structural performance, ensuring service safety, and reducing maintenance costs [3].…”

Section: Introductionmentioning

confidence: 99%

Fatigue Crack and Residual Life Prediction Based on an Adaptive Dynamic Bayesian Network

Chen,

Ma,

Wang

et al. 2024

Applied Sciences

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Monitoring the health status of aerospace structures during their service lives is a critical endeavor, aimed at precisely evaluating their operational condition through observation data and physical modeling. This study proposes a probabilistic assessment approach utilizing Dynamic Bayesian Networks (DBNs), enhanced by an improved adaptive particle filtering technique. This approach combines physical modeling with various predictive sources, encompassing cognitive uncertainties inherent in stochastic predictions and crack propagation forecasts. By employing crack observation data, it facilitates predictions of crack growth and the residual life of metal structure. To demonstrate the efficacy of this method, the research leverages data from three-point bending and single-edge tension fatigue tests. It gathers data on crack length during the fatigue crack progression, integrating these findings with digital twin theory to forecast the residual fatigue life of the specimens. The outcomes show that the adaptive DBN model can precisely predict fatigue crack propagation in test specimens, offering a potential tool for the online health assessment and life evaluation for aerospace structures.

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Section: Introductionmentioning

confidence: 99%

Fatigue Crack and Residual Life Prediction Based on an Adaptive Dynamic Bayesian Network

Chen,

Ma,

Wang

et al. 2024

Applied Sciences

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“…But besides this hardware point of view on the lifetime, the WSN reliability is also determined by its capability to ensure the required data quality. Different authors, therefore, addressed the topic of reliability in data acquisition processes with WSN from different points of view on hardware lifetime, energy consumption and data transmission, as reviewed in [15]. Most publications in the field of reliability in WSNs focus on the data point of view by addressing the problems of data loss, data delay, and data accuracy, and energy consumption is often considered [15].…”

Section: Introductionmentioning

confidence: 99%

Reliability Assessment of Wireless Sensor Networks by Strain-Based Region Analysis for Redundancy Estimation in Measurements on the Example of an Aircraft Wing Box

Meyer zu Westerhausen,

Raveendran,

Lauth

et al. 2024

Sensors

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Wireless sensor networks (WSNs) are attracting increasing research interest due to their ability to monitor large areas independently. Their reliability is a crucial issue, as it is influenced by hardware, data, and energy-related factors such as loading conditions, signal attenuation, and battery lifetime. Proper selection of sensor node positions is essential to maximise system reliability during the development of products equipped with WSNs. For this purpose, this paper presents an approach to estimate WSN system reliability during the development phase based on the analysis of measurements, using strain measurements in finite element (FE) models as an example. The approach involves dividing the part under consideration into regions with similar strains using a region growing algorithm (RGA). The WSN configuration is then analysed for reliability based on data paths and measurement redundancy resulting from the sensor positions in the identified measuring regions. This methodology was tested on an exemplary WSN configuration at an aircraft wing box under bending load and found to effectively estimate the hardware perspective on system reliability. Therefore, the methodology and algorithm show potential for optimising sensor node positions to achieve better reliability results.

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Structural Health Monitoring of Aerospace Composites

Cited by 2 publications

References 74 publications

Fatigue Crack and Residual Life Prediction Based on an Adaptive Dynamic Bayesian Network

Fatigue Crack and Residual Life Prediction Based on an Adaptive Dynamic Bayesian Network

Reliability Assessment of Wireless Sensor Networks by Strain-Based Region Analysis for Redundancy Estimation in Measurements on the Example of an Aircraft Wing Box

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