Reference : Structural damage diagnosis under varying environmental conditions - Part II: local P...
Scientific journals : Article
Engineering, computing & technology : Multidisciplinary, general & others
Engineering, computing & technology : Electrical & electronics engineering
Engineering, computing & technology : Mechanical engineering
http://hdl.handle.net/2268/18986
Structural damage diagnosis under varying environmental conditions - Part II: local PCA for non-linear cases
English
Yan, A. M. [> > > >]
Kerschen, Gaëtan mailto [Université de Liège - ULg > Département d'aérospatiale et mécanique > Laboratoire de structures et systèmes spatiaux >]
De Boe, P. [> > > >]
Golinval, Jean-Claude mailto [Université de Liège - ULg > Département d'aérospatiale et mécanique > LTAS - Vibrations et identification des structures >]
Jul-2005
Mechanical Systems & Signal Processing
Academic Press Ltd Elsevier Science Ltd
19
4
865-880
Yes (verified by ORBi)
International
0888-3270
London
[en] structural health monitoring ; effects of environmental variations ; local principal component analysis ; bridge diagnosis
[en] It is well known that changes in vibration features of structures due to damage may be masked by the effects of environmental variations. This influence has to be eliminated in the structural health-monitoring process, especially when a long-term in situ monitoring is expected. In the companion paper [1] a linear method based on principal component analysis (PCA) has been proposed and has shown encouraging results for linear or even weakly non-linear cases. The present paper concerns a further extension of the proposed method to handle non-linear cases, which may be encountered in some complex structures. The method involves a two-step procedure, namely a clustering of the data space into several regions and then the application of PCA La each local region. The application of local PCA allows performing a piecewise linearisation of the non-linear problem. A close look at the choice of the distortion function used in data clustering leads to two new clustering strategies. Whereas the first strategy is specifically suitable for the application treated in this paper, the second one is more general. The local PCA-based damage detection method is applied for the structural health monitoring of a real bridge using vibration data measured in situ over a one-year period. 2005 Elsevier Ltd. All rights reserved.
Researchers ; Professionals ; Students
http://hdl.handle.net/2268/18986
10.1016/j.ymssp.2004.12.003

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