Bootstrap tests for model selection in robust vibration analysis of oscillating structures

verfasst von
Boris Kargoll, Mohammad Omidalizarandi, Jens-André Paffenholz, Ingo Neumann, Gael Kermarrec, Hamza Alkhatib

In this contribution, a procedure for deciding,whether the oscillation of a surveyed structure is damped or not, is proposed. For this purpose, two bootstrap tests under fairly general assumptions regarding auto-correlation and outlier-affliction of the random deviations (“measurement errors”) are suggested. These tests are derived from an observation model consisting of (1) a parametric oscillation model based on trigonometric functions, (2) a parametric auto-correlation model in the form of an autoregressive process, and (3) a parametric stochastic model in terms of the heavy-tailed family of scaled t-distributions. These three levels, which generalize current observation models for oscillating structures, are jointly expressed as a likelihood function and jointly adjusted by means of a generalized expectation maximization algorithm. Closed-loop Monte Carlo simulations are performed to validate the bootstrap tests. Visual inspection of models fitted by standard least-squares techniques are shown to be insufficient to detect a small significant damped oscillation. Furthermore, the tests are applied to a controlled experiment in a laboratory environment. The oscillation was generated by means of a portable shaker vibration calibrator and measured by a reference accelerometer and a low-cost accelerometer.

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