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Towards robust statistical damage localization via model-based sensitivity clustering
- Source :
- Mechanical Systems and Signal Processing, Mechanical Systems and Signal Processing, 2019, 134, 25p, Mechanical Systems and Signal Processing, 2019, 134, pp.106341. ⟨10.1016/j.ymssp.2019.106341⟩, Mechanical Systems and Signal Processing, Elsevier, 2019, 134, pp.106341. ⟨10.1016/j.ymssp.2019.106341⟩
- Publication Year :
- 2019
- Publisher :
- HAL CCSD, 2019.
-
Abstract
- Damage diagnosis is a fundamental task for structural health monitoring (SHM). With the statistical sensitivity-based damage localization approach, a residual vector is computed from vibration measurements in the reference and the damaged state. The residual is analyzed statistically in hypothesis tests with respect to change directions defined by the sensitivities of the structural parameters associated to elements of a finite element (FE) model of the investigated structure. If the test for a parameter reacts, then the respective element of the structure is indicated as damaged. This approach offers a very generic and theoretically sound framework to analyze parametric changes in systems, and takes into account the intrinsic statistical uncertainty related to measurement data. Depending on the definition of the residual and of the parameterization, the approach offers a simple computation of the test statistics directly from the measurement data in the damaged system, without the need of system identification. Since an FE model is used, the approach is applicable on arbitrary structures, while no model updating is required and therefore the requirements on the FE model accuracy are less strict. While the theoretical framework has been developed previously, it lacked robustness so far for an application on real structures. The purpose of this paper is the development of this framework into a working damage localization method that is applicable on real data from complex structures. To achieve this goal, robust hypothesis tests are used, the sensitivity computation of the residual is revisited for more precision thanks to reduced modal truncation errors, and an adequate clustering approach is proposed for the case of a high-dimensional FE parameterization for complex structures. Furthermore, several robustness properties of the method are proven. Finally, an application of this framework is shown for the first time on experimental data for damage localization, namely in an ambient vibration test of a 3D steel frame at the University of British Columbia.
- Subjects :
- 0209 industrial biotechnology
STRUCTURE
Computer science
SECURITE STRUCTURELLE
Aerospace Engineering
SUBSPACE METHODS
DAMAGE LOCALIZATION
02 engineering and technology
Residual
01 natural sciences
STRUCTURAL VIBRATION MONITORING
020901 industrial engineering & automation
DIAGNOSTIC
[PHYS.MECA.STRU]Physics [physics]/Mechanics [physics]/Structural mechanics [physics.class-ph]
Robustness (computer science)
0103 physical sciences
INSTRUMENTATION
Sensitivity (control systems)
RESISTANCE DES MATERIAUX
VIBRATION MECANIQUE
Cluster analysis
AMBIENT EXCITATION
010301 acoustics
ComputingMilieux_MISCELLANEOUS
Civil and Structural Engineering
Statistical hypothesis testing
Parametric statistics
RESISTANCE MECANIQUE
[STAT.AP]Statistics [stat]/Applications [stat.AP]
Mechanical Engineering
System identification
SYSTEME MECANIQUE
MODELISATION
Computer Science Applications
Control and Systems Engineering
Signal Processing
[SPI.GCIV.DV]Engineering Sciences [physics]/Civil Engineering/Dynamique, vibrations
HYPOTHESIS TESTING
Structural health monitoring
DOMMAGE MATERIEL
Algorithm
Subjects
Details
- Language :
- English
- ISSN :
- 08883270 and 10961216
- Database :
- OpenAIRE
- Journal :
- Mechanical Systems and Signal Processing, Mechanical Systems and Signal Processing, 2019, 134, 25p, Mechanical Systems and Signal Processing, 2019, 134, pp.106341. ⟨10.1016/j.ymssp.2019.106341⟩, Mechanical Systems and Signal Processing, Elsevier, 2019, 134, pp.106341. ⟨10.1016/j.ymssp.2019.106341⟩
- Accession number :
- edsair.doi.dedup.....812fb7707af1ea04bbc689c5bc439cb5