Data-driven methodology to detect and classify structural changes under temperature variations
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2014-02-28
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Abstract
This paper presents a methodology for the detection and classification of structural changes
under different temperature scenarios using a statistical data-driven modelling approach by
means of a distributed piezoelectric active sensor network at different actuation phases. An
initial baseline pattern for each actuation phase for the healthy structure is built by applying
multiway principal component analysis (MPCA) to wavelet approximation coefficients
calculated using the discrete wavelet transform (DWT) from ultrasonic signals which are
collected during several experiments. In addition, experiments are performed with the
structure in different states (simulated damages), pre-processed and projected into the different
baseline patterns for each actuator. Some of these projections and squared prediction errors
(SPE) are used as input feature vectors to a self-organizing map (SOM), which is trained and
validated in order to build a final pattern with the aim of providing an insight into the classified
states. The methodology is tested using ultrasonic signals collected from an aluminium plate
and a stiffened composite panel. Results show that all the simulated states are successfully
classified no matter what the kind of damage or the temperature is in both structures.
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