Noise and interference in sensor measurements degrade the quality of data and have a negative impact on the
performance of structural damage diagnosis systems. In this paper, a novel adaptive measurement screening
approach is presented to automatically select the most informative measurements and use them intelligently for
structural damage estimation. The method is implemented efficiently in a sequential Monte Carlo (SMC) setting
using particle filtering. The noise suppression and improved damage estimation capability of the proposed method
is demonstrated by an application to the problem of estimating progressive fatigue damage in an aluminum
compact-tension (CT) sample using noisy PZT sensor measurements.
We propose a sequential Monte Carlo (SMC) based progressive structural damage diagnosis framework that
tracks damage by integrating information from physics-based damage evolution models and using stochastic
relationships between the measurements and the damage. The approach described in this paper adaptively
configures the sensors used to collect the measurements using the minimum predicted mean squared error (MSE)
as the performance metric. Optimization is performed globally over the entire search space of all available
sensors. Results are presented for the diagnosis of fatigue damage in a notched laminate, demonstrating the
effectiveness of the proposed method.
We describe a statistical method for the classification of damage in complex structures. Our approach is based
on a Bayesian framework using hidden Markov models (HMMs) to model time-frequency features extracted from
structural data. We also propose two different methods for sensor fusion to combine information from multiple
distributed sensors such that the overall classification performance is increased. The proposed approaches are
applied to the classification and localization of delamination in a laminated composite plate. Results using
both discrete and continuous observation density HMMs, together with the sensor fusion, are presented and
discussed.
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