Subspace identification algorithms have proven efficient for performing output-only identification of the eigenstructure of a linear mulit-input multi-output (MIMO) system subject to uncontrolled, unmeasured, and nons...
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Subspace identification algorithms have proven efficient for performing output-only identification of the eigenstructure of a linear mulit-input multi-output (MIMO) system subject to uncontrolled, unmeasured, and nonstationary excitation. Such a problem arises in mechanical engineering for modal analysis of vibrating structures. A common practice there is to collect data from varying sensor locations, using both fixed and moving sensors, in order to mimic the availability of a larger set of sensors. The purpose of this paper is to investigate how subspace-based identification can be adapted to handle such a situation, to prove its consistency under nonstationary excitation, and to report on a real application example.
Subspace identification algorithms are efficient for output-only eigenstructureidentification of linear MIMO systems. The problem of merging sensor data obtained from moving and non-simultaneously recorded measuremen...
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Subspace identification algorithms are efficient for output-only eigenstructureidentification of linear MIMO systems. The problem of merging sensor data obtained from moving and non-simultaneously recorded measurement setups under varying excitation is considered. To address the problem of dimension explosion, when retrieving the system matrices of the complete system, a modular and scalable approach is proposed. Adapted to a large class of subspace methods, observability matrices are normalized and merged to retrieve global system matrices.
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