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RISS 인기검색어
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- 저자 : Said Quqa (검색결과 3 건)
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Clump interpolation error for the identification of damage using decentralized sensor networks
Said Quqa,Pier Francesco Giordano,Maria Pina Limongelli,Luca Landi,Pier Paolo Diotallevi 국제구조공학회 2021 Smart Structures and Systems, An International Jou Vol.27 No.2
Recent developments in the field of smart sensing systems enable performing simple onboard operations which are increasingly used for the decentralization of complex procedures in the context of vibration-based structural health monitoring (SHM). Vibration data collected by multiple sensors are traditionally used to identify damage-sensitive features (DSFs) in a centralized topology. However, dealing with large infrastructures and wireless systems may be challenging due to their limited transmission range and to the energy consumption that increases with the complexity of the sensing network. Local DSFs based on data collected in the vicinity of inspection locations are the key to overcome geometric limits and easily design scalable wireless sensing systems. Furthermore, the onboard pre-processing of the raw data is necessary to reduce the transmission rate and improve the overall efficiency of the network. In this study, an effective method for real-time modal identification is used together with a local approximation of a damage feature, the interpolation error, to detect and localize damage due to a loss of stiffness. The DSF is evaluated using the responses recorded at small groups of sensors organized in a decentralized topology. This enables the onboard damage identification in real time thereby reducing computational effort and memory allocation requirements. Experimental tests conducted using real data confirm the robustness of the proposed method and the potential of its implementation onboard decentralized sensor networks.
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Alessandra Achilli,Giacomo Bernagozzi,Raimondo Betti,Pier Paolo Diotallevi,Luca Landi,Said Quqa,Eleonora M. Tronci 국제구조공학회 2021 Smart Structures and Systems, An International Jou Vol.27 No.2
This paper proposes a novel method suitable for vibration-based damage identification of civil structures and infrastructures under ambient excitation. The damage-sensitive feature employed in the presented algorithm consists of a vector of multivariate autoregressive parameters estimated from the vibration responses collected at different locations of the analyzed structure. Outlier analysis and statistical pattern recognition are exploited for damage detection and localization. In particular, the Mahalanobis distance between a set of reference (i.e., “healthy”) and inspection parameters is evaluated. A threshold is then selected to determine whether the inspection vectors refer to damaged or undamaged conditions. The effectiveness of the proposed approach is proved using numerical simulations and experimental data from a benchmark test. The analysis results show that the largest values of Mahalanobis distance can be found in the proximity of those sensors closest to the damaged elements. Thus, the Mahalanobis distance applied to vectors of multivariate autoregressive parameters has proven to be a robust indicator for damage detection and localization.
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A semi-supervised interpretable machine learning framework for sensor fault detection
Panagiotis Martakis,Artur Movsessian,Yves Reuland,Sai G.S. Pai,Said Quqa,David Garcıa Cava,Dmitri Tcherniak,Eleni Chatzi 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.29 No.1
Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easyto-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.
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