Vibration-based damage assessment using linear approximation with maximum entropy

Vibration-based damage assessment using linear approximation with maximum entropy

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  • July 22, 2014
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14th Pan-American Congress of Applied Mechanics – PACAM XIV
March 24-28, 2014, Santiago, Chile
Proceedings of PACAM XIV

V. Meruane and A. Ortiz-Bernardin

Abstract

Supervised learning algorithms have been proposed as a suitable alternative to model updating methods in vibration-based damage assessment, being Artificial Neural Networks the most frequently used. Notwithstanding, the slow learning speed and the large number of parameters that need to be tuned within the training stage have been a major bottleneck in their application. This article presents a new supervised learning algorithm for real-time damage assessment that uses a linear approximation method in conjunction with vibration characteristics measured from the damaged structure. The linear approximation is handled by a statistical inference model based on the maximum-entropy principle. The merits of this new approach are twofold: training is avoided and data is processed in a period of time that is comparable to the one of Neural Networks. The performance of the proposed methodology is validated by considering two experimental structures: an eight-degree-of-freedom (DOF) mass-spring system and an exhaust system of a car

Keywords: Structural damage assessment, supervised learning algorithms, maximum entropy principle, linear approximation

NOTICE: this is the author’s version of a work that was accepted for publication. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document.

NOTICE: this is the author’s version of a work that was accepted for publication. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document.

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