Statistical damage diagnosis in smart systems via contact-free MetGlas® sensors and stochastic non-linear modelling of system output data
by Dimitrios G. Dimogianopoulos, Dionysios E. Mouzakis, Dimitrios Kouzoudis
International Journal of Materials and Product Technology (IJMPT), Vol. 41, No. 1/2/3/4, 2011

Abstract: A contact-free, non-destructive concept for damage diagnosis in smart systems is introduced. It utilises in-house developed magnetoelastic contact-free sensors, providing output measurements of the system under load without bearing any physical contact with it. The system's health state is diagnosed via a specifically developed data processing scheme: firstly, the measured data are modelled via stochastic non-linear autoregressive (NAR) representations for capturing the health state-related system dynamics, and secondly, advanced statistical decision-making tests are used for evaluating this information and concluding on the system's health state. The experiments involve smart systems (formed by magnetoelastic MetGlas® alloy stripes attached to polymer epoxy resin slabs) undergoing vibration testing of growing amplitude in a dynamic mechanical analyser. Output data from such 'healthy' and 'damaged' systems are then assessed using the scheme, and finally, detection and severity evaluation, that is diagnosis, of damage are reliably concluded.

Online publication date: Sat, 28-Feb-2015

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