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Search Results (2)

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Keywords = modal Kalman filter with unknown inputs

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19 pages, 4895 KB  
Article
Strain Virtual Sensing for Structural Health Monitoring under Variable Loads
by Bartomeu Mora, Jon Basurko, Iman Sabahi, Urko Leturiondo and Joseba Albizuri
Sensors 2023, 23(10), 4706; https://doi.org/10.3390/s23104706 - 12 May 2023
Cited by 12 | Viewed by 2737
Abstract
Virtual sensing is the process of using available data from real sensors in combination with a model of the system to obtain estimated data from unmeasured points. In this article, different strain virtual sensing algorithms are tested using real sensor data, under unmeasured [...] Read more.
Virtual sensing is the process of using available data from real sensors in combination with a model of the system to obtain estimated data from unmeasured points. In this article, different strain virtual sensing algorithms are tested using real sensor data, under unmeasured different forces applied in different directions. Stochastic algorithms (Kalman filter and augmented Kalman filter) and deterministic algorithms (least-squares strain estimation) are tested with different input sensor configurations. A wind turbine prototype is used to apply the virtual sensing algorithms and evaluate the obtained estimations. An inertial shaker is installed on the top of the prototype, with a rotational base, to generate different external forces in different directions. The results obtained in the performed tests are analyzed to determine the most efficient sensor configurations capable of obtaining accurate estimates. Results show that it is possible to obtain accurate strain estimations at unmeasured points of a structure under an unknown loading condition, using measured strain data from a set of points and a sufficiently accurate FE model as input and applying the augmented Kalman filter or the least-squares strain estimation in combination with modal truncation and expansion techniques. Full article
(This article belongs to the Section Remote Sensors)
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16 pages, 6008 KB  
Article
Identification of Wind Loads on Structures Based on Modal Kalman Filter with Unknown Inputs
by Pingnan Zhao, Lijun Liu and Ying Lei
Buildings 2022, 12(7), 1003; https://doi.org/10.3390/buildings12071003 - 13 Jul 2022
Cited by 4 | Viewed by 2805
Abstract
Wind loads on structures are difficult to directly measure, so it is practical to identify structural wind loads based on the measurements of structural responses. However, this inversed problem is challenging compared with conventional load identification as wind loads are time-space coupled and [...] Read more.
Wind loads on structures are difficult to directly measure, so it is practical to identify structural wind loads based on the measurements of structural responses. However, this inversed problem is challenging compared with conventional load identification as wind loads are time-space coupled and spatially distributed dynamic loads on structures. An improved method is proposed for identifying wind loads on structures using only partial measurements of structural acceleration responses in this paper. First, the wind loads on a structure are decomposed by proper orthogonal decomposition as a series of time-space decoupled sub-distributed dynamic loads with independent basic spatial distribution functions and time history functions. Herein, structural modes are adopted as the basic spatial distribution functions and structural modes of discretized and continuous structural systems are investigated. Then, a history function of the decomposed wind load is identified in the modal domain based on modal Kalman filter with unknown inputs, which is proposed by the authors. Finally, the distributed wind loads are reconstructed for discrete or continuous structural systems. The feasibility of the proposed algorithm is verified by two numerical examples of identification of wind loads on a discrete shear frame and a wind turbine tower, respectively. Full article
(This article belongs to the Special Issue Structural Health Monitoring)
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