The Application of Machine Learning to Structural Health Monitoring

The Application of Machine Learning to Structural Health Monitoring

In broad terms, there are two approaches to damage identification. Model-driven methods establish a high-fidelity physical model of the structure, usually by finite element analysis, and then establish a comparison metric between the model and the measured data from the real structure. If the model...

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Bibliographische Detailangaben
Veröffentlicht in:Philosophical Transactions: Mathematical, Physical and Engineering Sciences. - The Royal Society. - 365(2007), 1851, Seite 515-537
1. Verfasser: Worden, Keith (VerfasserIn)
Weitere Verfasser: Manson, Graeme
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2007
Zugriff auf das übergeordnete Werk:Philosophical Transactions: Mathematical, Physical and Engineering Sciences
Schlagworte:Structural health monitoring Machine learning Pattern recognition Neural networks Statistical learning theory Support vector machines Applied sciences Physical sciences Mathematics Behavioral sciences
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520 |a In broad terms, there are two approaches to damage identification. Model-driven methods establish a high-fidelity physical model of the structure, usually by finite element analysis, and then establish a comparison metric between the model and the measured data from the real structure. If the model is for a system or structure in normal (i.e. undamaged) condition, any departures indicate that the structure has deviated from normal condition and damage is inferred. Data-driven approaches also establish a model, but this is usually a statistical representation of the system, e.g. a probability density function of the normal condition. Departures from normality are then signalled by measured data appearing in regions of very low density. The algorithms that have been developed over the years for data-driven approaches are mainly drawn from the discipline of pattern recognition, or more broadly, machine learning. The object of this paper is to illustrate the utility of the data-driven approach to damage identification by means of a number of case studies. 
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