Subspace-based system identification and fault detection: Algorithms for large systems and application to structural vibration analysis
type: Dataset
description: System identification methods are especially attractive to structural engineering for the identification of vibration modes and mode shapes of structures, as well as for detecting changes in their vibration characteristics, both under real operation conditions. Focusing on the class of subspace-based methods, the goal of this thesis is to improve the efficiency and robustness of system identification and fault detection of large in-operational structures, which are heavily instrumented and work under noisy and varying environmental conditions. In this thesis, four different hurdles are cleared. Firstly, an algorithm is derived for directly merging sensor data from multiple measurement sessions at different sensor positions and under different excitation conditions. With a modular and memory efficient approach, global subspace-based system identification of large structures is possible. Secondly, a reformulation of a least squares problem leads to a significantly faster computation of system identification results at multiple model orders, which is used to distinguish true physical modes of a structure from spurious modes under in-operation conditions. Thirdly, a statistical subspace-based fault detection method is improved using a residual that is robust to changes in the unmeasured ambient excitation. Finally, a statistical damage localization test is derived, where required sensitivities are computed from measured data without the need of finite element model. The proposed methods are validated on simulations and are successfully applied to system identification and damage detection of several large-scale civil structures.
landing page: https://theses.hal.science/tel-00953781
keyword: analyse-modale
keyword: detection-et-diagnostic-de-pannes
keyword: fault-detection-and-isolation
keyword: identification-des-systemes
keyword: infoeu-reposemanticsdoctoralthesis
keyword: linear-systems
keyword: mathmath-dsmathematics-mathdynamical-systems-mathds
keyword: methodes-sous-espace
keyword: modal-analysis
keyword: physmecavibrphysics-physicsmechanics-physicsvibrations-physicsclass-ph
keyword: spimecavibrengineering-sciences-physicsmechanics-physicsmed-phvibrations-physicsclass-p
keyword: statapstatistics-statapplications-statap
keyword: subspace-methods
keyword: system-identification
keyword: systemes-lineaires
keyword: theses
keyword: vibration
issued: 2026-05-06T04:44:36.693816
modified: 2026-05-06T04:44:36.693821
language: en
contact point: Organization
  fn: CCSD

test_moissonnage_selune
type: Agent

Subspace-based system identification and fault detection: Algorithms for large systems and application to structural vibration analysis
type: Distribution
format: HTML
access URL: https://theses.hal.science/tel-00953781
issued: 2026-05-06T04:44:36.698962
modified: 2026-05-06T04:44:36.675113