An automatic system for on-line change detection with application to structural health monitoring

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2009

Autores

Hugo Alonso (Autor) FEUP; Pedro Ribeiro (Autor) FEUP; Paula Rocha (Autor) FEUP

Ata de Conferência Internacional

Título: IECON: 2009 35TH ANNUAL CONFERENCE OF IEEE INDUSTRIAL ELECTRONICS, VOLS 1-6

Páginas: 3309-3314

35th Annual Conference of the IEEE-Industrial-Electronics-Society (IECON 2009)

Porto, PORTUGAL, NOV 03-05, 2009

Indexação

ISI Proceedings

ISI Web of Knowledge - 0 Citações

Scopus - 4 Citações

INSPEC

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Engenharia electrotécnica, electrónica e informática

CORDIS: Ciências Tecnológicas > Engenharia > Engenharia electrónica

Outras Informações

ID Authenticus: P-003-QFX

DOI: 10.1109/iecon.2009.5415055

Abstract (EN): This paper presents a new automatic system for on-line change detection in structural health monitoring. The system is based on a combination of a Hopfield neural network with an adaptive kernel density estimation method and a test for unimodality. The changes in the process being monitored are detected from a model of that process. A Hopfield neural network is used on-line to adapt the model, tracking parameter variations from the process data. Given that these data are often corrupted by random noise, the parameter estimator implemented by the network can be regarded as a random vector. In this context,. an adaptive kernel density estimation method is used to estimate the marginal probability density functions of the parameter estimator. When a parameter changes, the corresponding estimator marginal density becomes nonunimodal and this change is automatically detected by a test for unimodality. The robustness of the proposed system is guaranteed by the robustness of both the network and density estimation method. The system performance in structural health monitoring is illustrated by means of a simulation study, where a comparison is carried out with another approach to the problem of on-line change detection.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6