Title: Statistical damage diagnosis in smart systems via contact-free MetGlas® sensors and stochastic non-linear modelling of system output data

Authors: Dimitrios G. Dimogianopoulos, Dionysios E. Mouzakis, Dimitrios Kouzoudis

Addresses: Stochastic Mechanical Systems and Automation (SMSA) Laboratory, Department of Mechanical and Aeronautical Engineering, University of Patras, GR 26504, Greece. ' Department of Materials Science, University of Patras, GR 26504, Greece. ' Department of Engineering, University of Patras, GR 26504, Greece

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.

Keywords: magnetoelastic materials; nondestructive evaluation; NDE; contact-free sensors; MetGlas alloys; smart systems; damage detection; damage severity estimation; statistical decision making; stochastic systems; nonlinear modelling; fault diagnosis; vibration testing.

DOI: 10.1504/IJMPT.2011.040285

International Journal of Materials and Product Technology, 2011 Vol.41 No.1/2/3/4, pp.39 - 60

Published online: 28 Feb 2015 *

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