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Varied System Geometry and Noise Implementation Applied to Nonlinear Model Tracking

[+] Author Affiliations
Timothy A. Doughty, Liam J. Cassidy, Shannon M. Danforth, Nicholas Pendowski

University of Portland, Portland, OR

Paper No. IMECE2016-65987, pp. V04AT05A058; 6 pages
doi:10.1115/IMECE2016-65987
From:
  • ASME 2016 International Mechanical Engineering Congress and Exposition
  • Volume 4A: Dynamics, Vibration, and Control
  • Phoenix, Arizona, USA, November 11–17, 2016
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-5054-1
  • Copyright © 2016 by ASME

abstract

The following is a study in nondestructive health monitoring wherein the physical system being studied is excited near resonance and mapped through its transition from health to failure. The system studied is a slender cantilever beam excited near its second natural frequency. For this study, no damage is initiated and so it comes in contrast to the more common techniques where the damage type and location allow for an element of control in instrumentation and analysis. The method implemented allows for health monitoring in situ, so it does not require stopping the event to do system testing, as is the case for many common approaches. Moreover, this method, implements a nonlinear model of the physical system, avoiding false flags that can be problematic for linear-based methods when applied to systems demonstrating healthy nonlinear behavior. The method, known as Nonlinear Model Tracking (NMT) uses a theoretical model of the system that includes a cubic nonlinear stiffness term. Experimentally, stimulus and response data are collected and used in Continuous Time-based system identification to estimate the system’s nonlinear stiffness coefficient. Harmonic fitting to the two recorded data sets allow for robust performance in the presence of noise and variations in the system geometry show that, even in cases where the nonlinear model is not accurate for the system being studied, the method works consistently. In many of the tests the method gives premonition of failure hours in advance, which would in many real world scenarios, gives users time to react safely. This study focusses particularly on varying inputs to the system and attempting to map changes in parameter estimation to stages of damage.

Copyright © 2016 by ASME

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