Abstract
This paper discusses the possibility of using the Mahalanobis squared-distance to perform robust novelty detection in the presence of important variability in a multivariate feature vector. The application of interest is vibration-based structural health monitoring with a focus on data-based damage detection. For this application, the Mahalanobis distance can be used to detect novelty using a multivariate feature vector extracted from vibration measurements from a structure at regular intervals during its lifetime. One of the major problems is that changing environmental conditions induce large variability in the feature vector under normal condition, which usually prevents detection of smaller variations due to damage. In this paper, it is shown that including the variability due to the environment in the training data used to define the Mahalanobis distance results in very efficient filtering of the environmental effects while keeping the sensitivity to structural changes.
Highlights
Vibration-based Structural Health Monitoring (SHM) techniques have been around for many years and are still today an active topic of research
A structural change is induced in the form of an added mass, and the results show that by including the environmental variability in the computation of the covariance matrix, the Mahalanobis distance filters it effectively while keeping a high sensitivity to the damage
When including all the variability from the environment in the computation of the covariance matrix, robust novelty detection is achieved: the Mahalanobis distance is insensitive to environmental changes and very sensitive to structural changes
Summary
Vibration-based Structural Health Monitoring (SHM) techniques have been around for many years and are still today an active topic of research. This paper deals with the third element of the databased damage detection system and focuses on the use of the Mahalanobis squared-distance An interpretation of this distance is given by performing an eigenvalue decomposition of the covariance matrix used to compute it. The subspace including the variability corresponds to the directions in the first set with the largest eigenvalues, and the Mahalanobis distance is almost insensitive to it This idea is illustrated on the example of a wooden bridge in which the modal data varies significantly due to environmental conditions. A structural change is induced in the form of an added mass, and the results show that by including the environmental variability in the computation of the covariance matrix, the Mahalanobis distance filters it effectively while keeping a high sensitivity to the damage
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