Case-Based Reasoning for Stiffness Changes Detection in Structures: Numerical Validation by using Finite Element Model

Case-Based Reasoning for Stiffness Changes Detection in Structures: Numerical Validation by using Finite Element Model

Rodolfo Villamizar Mejia (Universidad Industrial de Santander, Colombia), Jhonatan Camacho Navarro (Universidad Industrial de Santander, Colombia) and Wilmer Alexis Sandoval Caceres (Universidad Industrial de Santander, Colombia)
Copyright: © 2015 |Pages: 29
DOI: 10.4018/978-1-4666-8490-4.ch007
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Abstract

This chapter presents an expert monitoring algorithm approach to detect, locate and quantify stiffness variations in structures. The algorithm is based on pattern recognition and artificial intelligence techniques that emulate knowledge based on human reasoning. The expert system (ES) uses time-frequency information about dynamics of structure, which is processed by using discrete wavelet transform (DWT), self-organizing maps (SOM), case-based reasoning (CBR) and principal component analysis (PCA). In addition, two applications are considered in order to evaluate the effectiveness of vibration analysis methodology and CBR in damage detection. The first application (Camacho 2010) uses the environmental excitation to detect and quantify damage in a Mechanical UBC ASCE Benchmark. The second one (Sandoval 2010) uses a predesigned signal to detect geometric damages on a gas pipeline. In both cases, a finite element model (FEM) is used to simulate different damages scenarios, which correspond to stiffness variations in different location.
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Background

The methodology described in this chapter emphasized the advantages of using vibrational based damage detection algorithms. Vibration based condition monitoring refers to the use of in situ non-destructive sensing and analysis of system characteristics –in the time, frequency or modal domains –for the purpose of detecting changes, which may indicate damage or degradation (Carden & Fanning, 2004). Vibration based damage assessment has been reported as successful for a great variety of applications, which include structures as spacecraft components (Mujica, 2006), bridges (Villamizar, 2005), aircrafts, beams (Begambre, 2004), offshore structures and railroads, among others (Yolken & Matzkanin, 2008). The damage diagnosis is achieved by configuring in a proper way three major components (Li et. al., 2014): Sensor system, a data processing system and a health evaluation system. The reported literature can be divided into three categories (Sinou, 2009): modal testing, vibration-based methods with signal-based and model-based methods, and non-traditional methods.

Modal testing refers to methods where damage assessment is based on identifying changes in estimated modal parameters such as frequencies and modes shapes (Hatch, 2001; Fan & Qiao, 2011). As example, the effectiveness of damage identification methods based on natural excitation Technique (NeXT) using ambient vibration it is demonstrated (Caicedo, 2011).

In signal-based methods, different statistical indexes of the time structure response to a specific excitation (vibration - forced) are used to distinguish between damage and undamaged states. Some of the applications involve experimental testing of structural fault detection algorithms based on features from principal component analysis and piezoelectric active systems, which are validated on pipeline structures, laminate plates, aircraft sections and composite materials, among others (Mujica et al., 2010).

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