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 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.
Worden
Keith
K
Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Mappin Street, Sheffield S1 3JD, UK. k.worden@sheffield.ac.uk
Manson
Graeme
G
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003252:Construction Materials / Q000032:analysis*; D004738:Engineering / Q000295:instrumentation / Q000379:methods; D004867:Equipment Design; D019544:Equipment Failure Analysis / Q000295:instrumentation / Q000379:methods*; D005160:Facility Design and Construction / Q000295:instrumentation / Q000379:methods*; D008283:Maintenance / Q000379:methods; D008422:Materials Testing / Q000379:methods*; D008962:Models, Theoretical; D012815:Signal Processing, Computer-Assisted; D014159:Transducers; D014732:Vibration