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Item type:Publication, Access status: without files , A decentralized digital twinning approach for wireless structural health monitoring systemsSince the introduction of wireless technologies in structural health monitoring (SHM), researchers have focused on exploiting the computational capabilities of wireless sensor nodes via embedded computing approaches that enable on-board data analysis, in lieu of wirelessly transmitting large amounts of data. Embedded computing for wireless SHM has largely been based on data-driven analysis methods, which only implicitly account for the physics governing the structural behavior. However, the increasing digitalization of societal activities, including structural maintenance, is frequently associated with digital twinning, which builds upon continuous exchange of data between the digital and the physical world and, often, upon physics-based modeling. In this context, this paper presents an approach for integrating digital twins into wireless SHM systems. Given the distributed nature of wireless sensor networks, characterized by the quasi-independent operation of wireless sensor nodes, the digital twins are designed in a decentralized manner, leveraging the computational power of wireless sensor nodes. In particular, the decentralized digital twins are represented by partial finite element (FE) models of a structure being monitored that are embedded in the wireless sensor nodes and “live” and “evolve” in tandem with the structure. The wireless sensor nodes use the partial FE models and structural response data locally collected to collaboratively assess the structural condition. The proposed approach is validated via laboratory tests, the results of which showcase the capability of the approach to yield reliable estimates of the structural condition.Publicationtype: Conference Paper21 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , An embedded computing approach for vibration-based system identification using reduced-order finite elementsSystem identification (SID) constitutes a focal point in experimental testing of civil infrastructure as well as in structural health monitoring (SHM). Conventional SID approaches have relied on creating finite element (FE) models of civil infrastructure assets and on calibrating the models against in-situ derived information from testing equipment or against SHM data. FE models are capable of providing valuable information on the structural condition. However, the equations governing the FE method are coupled, usually calling for centralized data collection, which may be unsuitable for modern wireless SHM systems that employ independently operating wireless sensor nodes. In this paper, a SID approach for making full use of FE models on board wireless sensor nodes is presented, based on embedded computing and on reduced-order finite elements. Specifically, FE models of civil infrastructure assets are reduced, using model-order reduction, and partitioned into partial models, which represent different substructures and are computationally efficient for embedment in wireless sensor nodes. Using the partial models and structural response data from the substructures, insights into the structural condition of the civil infrastructure assets are obtained. The proposed approach is suitable for vibration-based SID, applied as part of long-term SHM strategies, and implementable in wireless SHM systems. Validation tests using SHM data recorded from a real-world road bridge showcase the validity of the proposed SID approach.Publicationtype: Conference Paper14 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Physics-based wireless structural health monitoring and control using temporal finite elements(Institute of Structural Analysis and Antiseismic Research National Technical University of Athens, 2025-06); ; ; Integrating structural control into wireless structural health monitoring systems typically entails implementing control algorithms that predict counteracting forces for keeping the structural response within acceptable bounds. Control algorithms usually involve data-driven analysis, implemented in wireless sensor networks as feedback-loop systems. Although effective, data-driven analysis for structural control hardly accounts for the physics underlying the structural behavior. In an attempt to implement structural control in an intuitive manner for engineers, this work suggests an embedded structural control method that builds upon physics-based modeling, using the finite element method for analysis. In particular, the proposed physics-based wireless structural health monitoring and control (SHMC) method bases the prediction of control forces on temporal finite elements (TFE), which integrate the time dimension into the well-known FE spatial shape functions. Prior to embedment, the TFE models are reduced to (i) decrease the computational burden on the sensor nodes and (ii) match the discretization of the TFE models with the degrees of freedom measured by the SHMC system. The proposed SHMC method is implemented in a prototype wireless SHMC system and validated in laboratory tests on a model frame structure mounted on a shake table. The results showcase the capability of the physics-based wireless SHMC method to retain the structural response within previously defined bounds.Publicationtype: Conference Paper28 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Identification of combined sensor faults in structural health monitoring systems(IOP Publishing, 2024-07-19); ; ; ; Fault diagnosis (FD), comprising fault detection, isolation, identification and accommodation, enables structural health monitoring (SHM) systems to operate reliably by allowing timely rectification of sensor faults that may cause data corruption or loss. Although sensor fault identification is scarce in FD of SHM systems, recent FD methods have included fault identification assuming one sensor fault at a time. However, real-world SHM systems may include combined faults that simultaneously affect individual sensors. This paper presents a methodology for identifying combined sensor faults occurring simultaneously in individual sensors. To improve the quality of FD and comprehend the causes leading to sensor faults, the identification of combined sensor faults (ICSF) methodology is based on a formal classification of the types of combined sensor faults. Specifically, the ICSF methodology builds upon long short-term memory (LSTM) networks, i.e. a type of recurrent neural networks, used for classifying ‘sequences’, such as sets of acceleration measurements. The ICSF methodology is validated using real-world acceleration measurements from an SHM system installed on a bridge, demonstrating the capability of the LSTM networks in identifying combined sensor faults, thus improving the quality of FD in SHM systems. Future research aims to decentralize the ICSF methodology and to reformulate the classification models in a mathematical form with an explanation interface, using explainable artificial intelligence, for increased transparency.Publicationtype: Journal Article10 167 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , A deep-learning-based approach towards identifying combined faults in structural health monitoring(NDT.net, 2024-07-01); ; ; ; Fault diagnosis (FD), encompassing fault detection, isolation, identification and accommodation, is critical for reliable structural health monitoring (SHM) systems, ensuring correction of sensor faults that may corrupt or invalidate monitoring data. While sensor fault identification has received scarce attention within FD of SHM systems, recent methods have been proposed for identifying single sensor faults. Nonetheless, real-world SHM systems are prone to combined faults, i.e. different faults that affect individual sensors simultaneously. Identifying combined sensor faults is essential for improving the quality of FD and for gaining insight into the causes of sensor faults. This paper presents an approach for identifying combined sensor faults, referred to as ICSF approach, aiming to identify sensor faults occurring simultaneously in individual sensors using time-series data, thereby improving the quality of FD in SHM systems. Leveraging a recurrent neural network, specifically a long short-term memory network, a classification algorithm is implemented for mapping time-series data to combined sensor faults. The ICSF approach is validated using acceleration measurements collected by a faulty sensor from a real-world SHM system installed on a pedestrian bridge. The results demonstrate the effectiveness of the ICSF approach in identifying combined sensor faults, enhancing sensor FD in real-world SHM systems.Publicationtype: Conference Paper15 90 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Sensorintegrierte Digitale Zwillinge für das automatisierte Monitoring von Infrastrukturbauwerken(2022); ; Sensor-integrated digital twins for wireless structural health monitoring of civil infrastructure. Digital twins constitute a promising technology that lies at the core of emerging trends within Industry 4.0, such as cyber-physical systems and the Internet-of-Everything. The civil engineering community has been showing increasing interest in adopting digital twins for operation and management purposes, e. g. within the framework of structural health monitoring (SHM). In particular, SHM stands to benefit from the enhanced predictive capabilities of digital twins, which yield richer information on structural conditions than conventional models. However, adapting state-of-the-art digital twins to modern SHM strategies, which rely on wireless technologies, is hardly straightforward. Specifically, digital twins that model a physical process are usually centralized and updated using large amounts of data collected from the physical process. By contrast, wireless SHM systems are distributed, and wireless SHM strategies typically focus on minimizing wireless communication, which is unreliable and power-consuming. In this context, this paper presents the results of a feasibility study that discusses the practical considerations of embedding digital twins in wireless SHM systems. Through field tests on a pedestrian bridge, instrumented with a wireless SHM system, the decentralized embedment of a digital twin – in the form of partial digital twins – into the wireless sensor nodes is reported. The field test results indicate that decentralized digital twins can advantageously be used for advancing wireless SHM.Publicationtype: Journal Article32 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , On-board data synchronization in wireless structural health monitoring systems based on phase lockingWireless sensor networks are prone to synchronization discrepancies, due to the lack of intrinsic global clock management from a centralized server. In wireless structural health monitoring (SHM) systems, synchronization discrepancies may lead to erroneous estimations of structural parameters of monitored structures. To avoid errors in the estimations of structural parameters, structural response data sets collected from a structure must be synchronized. Synchronization between structural response data sets can be achieved through offline processing. However, in wireless SHM systems, offline processing requires wireless communication of entire structural response data sets, which has been proven detrimental to the power autonomy of wireless sensor nodes. This paper presents an embedded synchronization algorithm for wireless SHM systems. The embedded synchronization algorithm functions as a module added to embedded algorithms performing peak picking, which is part of operational modal analysis, for ensuring accurate outcomes. The embedded synchronization algorithm enables wireless SHM systems to synchronize structural response data sets on board using the embedded computing capabilities of wireless sensor nodes. The synchronization is achieved by imposing the expected relationship between the phase angles of Fourier spectra of acceleration response data sets at peaks corresponding to vibration modes. Time lags are autonomously estimated by the wireless sensor nodes through collaborative analysis of the phase angle relationship between acceleration response data sets collected by different sensor nodes. The embedded synchronization algorithm is implemented into a prototype wireless SHM system with embedded peak picking algorithms and validated by laboratory tests and by ambient vibration tests on a pedestrian bridge.Publicationtype: Journal Article85 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Monitoringgestützte Analyse von Schwingungsdämpfern in Turmbauwerken(2018-06); ; ; Monitoring-based performance analysis of tuned mass dampers in tower structures. Slender structures, such as towers and masts, frequently exhibit unfavourable responses when subjected to dynamic loads, characterized by the non-negligible contribution of higher modes of vibration as well as resonant phenomena between the loads and the structure. To mitigate such responses, tuned mass dampers (TMDs) have been employed in several slender structures. A TMD is a structural sub-system fixed to a structure and “tuned” to counteract a response frequency, which is anticipated to induce large oscillations to the structure, through its out-of-phase response. The design of tuned mass dampers is based on modelling assumptions and on sparse observations of the dynamic behaviour of structures, which changes due to structural ageing, thus rendering the regular performance assessment of TMDs is essential. In this paper, a methodology for assessing TMD performance using structural health monitoring (SHM) data is presented. Specifically, the TMD functionality is assessed upon obtaining the phase shift between the TMD response and the response of the structure, which, according to TMD theory, indicates favourable dampening effect when assuming values close to 90°. The proposed methodology is validated via a case study of a telecommunication tower equipped with a TMD and an SHM system, showcasing the ability of the methodology to assess TMD performance.Publicationtype: Journal Article44 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Distributed adaptive diagnosis of sensor faults using structural response data(2016-09-20); The reliability and consistency of wireless structural health monitoring (SHM) systems can be compromised by sensor faults, leading to miscalibrations, corrupted data, or even data loss. Several research approaches towards fault diagnosis, referred to as 'analytical redundancy', have been proposed that analyze the correlations between different sensor outputs. In wireless SHM, most analytical redundancy approaches require centralized data storage on a server for data analysis, while other approaches exploit the on-board computing capabilities of wireless sensor nodes, analyzing the raw sensor data directly on board. However, using raw sensor data poses an operational constraint due to the limited power resources of wireless sensor nodes. In this paper, a new distributed autonomous approach towards sensor fault diagnosis based on processed structural response data is presented. The inherent correlations among Fourier amplitudes of acceleration response data, at peaks corresponding to the eigenfrequencies of the structure, are used for diagnosis of abnormal sensor outputs at a given structural condition. Representing an entirely data-driven analytical redundancy approach that does not require any a priori knowledge of the monitored structure or of the SHM system, artificial neural networks (ANN) are embedded into the sensor nodes enabling cooperative fault diagnosis in a fully decentralized manner. The distributed analytical redundancy approach is implemented into a wireless SHM system and validated in laboratory experiments, demonstrating the ability of wireless sensor nodes to self-diagnose sensor faults accurately and efficiently with minimal data traffic. Besides enabling distributed autonomous fault diagnosis, the embedded ANNs are able to adapt to the actual condition of the structure, thus ensuring accurate and efficient fault diagnosis even in case of structural changes.Publicationtype: Journal Article26