On statistical Multi-Objective optimization of sensor networks and optimal detector derivation for structural health monitoring

Colombo, Luca; Todd, Michael D.; Sbarufatti, Claudio; Giglio, Marco

doi:10.1016/j.ymssp.2021.108528

Cited by 15 publications

(7 citation statements)

References 40 publications

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“…Based on task-based teaching method, the title, structure, quoted materials, and classical degree of one of the articles are questioned in three task stages of preclass, class, and after-class, respectively, to analyze and improve learners' critical thinking [21]. As for the research on the cultivation of logical thinking ability, it not only draws relevant conclusions based on case analysis, that is, the cultivation of comprehensive language ability is closely related to the improvement of logical thinking ability but also further discusses how to internalize the logical knowledge that students lack in normal teaching through high English teaching [22,23].…”

Section: Related Workmentioning

confidence: 99%

Advanced Intelligent English Translation Based on Multisensor Data Fusion Optimization

Wang

2022

Journal of Sensors

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English translation activity course is of great significance to cultivate students’ English translation level. In the context of multisensor data fusion, how to effectively carry out English translation activity course in colleges and universities has become an important topic. The educational value and intellectual property of advanced Intelligent English translation activity course are analyzed. From multisensor data fusion and the improvement of translation, translators psychological changes of the boot and prominent features, English translation activity and translation, the generality of the multisensor data fusion of multisensor data fusion personalization features this four aspects, which is under the background of the sensor data fusion of the practice for college English translation activity. Firstly, the theory of data fusion estimation is elaborated, and various data fusion structures in multisensor systems are summarized. Then, the data fusion estimation model based on Kalman filter is established, and the Kalman filtering algorithms of centralized, sequential, parallel, and joint structures are given, respectively. Simulation experiments are carried out on the algorithms. Experimental results show that the estimation accuracy of the system can be improved by multisensor data fusion. Then a rule-based lexical analyzer is designed. Combined with the system model, a rule-based lexical analyzer and a comprehensive dictionary, the linguistic knowledge source throughout the whole machine translation process, are researched and designed. A hashing algorithm for lexical retrieval is designed, and various rules and data structures related to the lexical analyzer are described in formal language. The analysis algorithms of morphological preprocessing, morphological analysis, unincluded word processing, phrase analysis, and part of speech tagging are introduced.

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Section: Related Workmentioning

confidence: 99%

Advanced Intelligent English Translation Based on Multisensor Data Fusion Optimization

Wang

2022

Journal of Sensors

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“…By analyzing computational models and historical data, these algorithms [86][87][88][89] can suggest the most strategic locations for sensors to maximize their effectiveness. This ensures that the acquired data are not only comprehensive but also highly relevant, enhancing the overall efficiency of the SHM system [90,91].…”

Section: Machine Learning As An Enabling Tool For the Cbm Of Srmsmentioning

confidence: 99%

Structural Health Monitoring of Solid Rocket Motors: From Destructive Testing to Perspectives of Photonic-Based Sensing

Korompili,

Mußbach,

Riziotis

2024

Instruments

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In the realm of space exploration, solid rocket motors (SRMs) play a pivotal role due to their reliability and high thrust-to-weight ratio. Serving as boosters in space launch vehicles and employed in military systems, and other critical & emerging applications, SRMs’ structural integrity monitoring, is of paramount importance. Traditional maintenance approaches often prove inefficient, leading to either unnecessary interventions or unexpected failures. Condition-based maintenance (CBM) emerges as a transformative strategy, incorporating advanced sensing technologies and predictive analytics. By continuously monitoring crucial parameters such as temperature, pressure, and strain, CBM enables real-time analysis, ensuring timely intervention upon detecting anomalies, thereby optimizing SRM lifecycle management. This paper critically evaluates conventional SRM health diagnosis methods and explores emerging sensing technologies. Photonic sensors and fiber-optic sensors, in particular, demonstrate exceptional promise. Their enhanced sensitivity and broad measurement range allow precise monitoring of temperature, strain, pressure, and vibration, capturing subtle changes indicative of degradation or potential failures. These sensors enable comprehensive, non-intrusive monitoring of multiple SRM locations simultaneously. Integrated with data analytics, these sensors empower predictive analysis, facilitating SRM behavior prediction and optimal maintenance planning. Ultimately, CBM, bolstered by advanced photonic sensors, promises enhanced operational availability, reduced costs, improved safety, and efficient resource allocation in SRM applications.

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“…An overview of the variety of approaches to the problem of optimal sensor placement (OSP) in vibration-based SHM presented in [ 15 ] clearly shows the necessity of developing effective algorithms focused on minimizing the number of sensors in the measurement network without losing the quality and accuracy of structural damage. The fundamentality of solving an OSP problem in SHM systems is underlined in many recent studies; see, e.g., [ 20 , 21 , 22 ]. Taking into account the variety of factors that influence OSP, which include numerous uncertainties, assumed criteria, optimization algorithms and strategies, the problem is not trivial, especially when it is implemented physically as an SHM system on a composite structure.…”

Section: Introductionmentioning

confidence: 99%

Monitoring of Damage in Composite Structures Using an Optimized Sensor Network: A Data-Driven Experimental Approach

Ručevskis

Rogala

Katunin

2023

Sensors

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Due to the complexity of the fracture mechanisms in composites, monitoring damage using a vibration-based structural response remains a challenging task. This is also complex when considering the physical implementation of a health monitoring system with its numerous uncertainties and constraints, including the presence of measurement noise, changes in boundary and environmental conditions of a tested object, etc. Finally, to balance such a system in terms of efficiency and cost, the sensor network needs to be optimized. The main aim of this study is to develop a cost- and performance-effective data-driven approach to monitor damage in composite structures and validate this approach through tests performed on a physically implemented structural health monitoring (SHM) system. In this study, we combined the mentioned research problems to develop and implement an SHM system to monitor delamination in composite plates using data combined from finite element models and laboratory experiments to ensure robustness to measurement noise with a simultaneous lack of necessity to perform multiple physical experiments. The developed approach allows the implementation of a cost-effective SHM system with validated predictive performance.

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On statistical Multi-Objective optimization of sensor networks and optimal detector derivation for structural health monitoring

Cited by 15 publications

References 40 publications

Advanced Intelligent English Translation Based on Multisensor Data Fusion Optimization

Advanced Intelligent English Translation Based on Multisensor Data Fusion Optimization

Structural Health Monitoring of Solid Rocket Motors: From Destructive Testing to Perspectives of Photonic-Based Sensing

Monitoring of Damage in Composite Structures Using an Optimized Sensor Network: A Data-Driven Experimental Approach

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