Data Mining for Structural Health Monitoring

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Author(s): Ramdev Kanapady (University of Minnesota, USA)and Aleksandar Lazarevic (United Technologies Research Center, USA)
Copyright: 2009
Pages: 8
Source title: Encyclopedia of Data Warehousing and Mining, Second Edition
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-60566-010-3.ch071

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Abstract

Structural health monitoring denotes the ability to collect data about critical engineering structural elements using various sensors and to detect and interpret adverse “changes” in a structure in order to reduce life-cycle costs and improve reliability. The process of implementing and maintaining a structural health monitoring system consists of operational evaluation, data processing, damage detection and life prediction of structures. This process involves the observation of a structure over a period of time using continuous or periodic monitoring of spaced measurements, the extraction of features from these measurements, and the analysis of these features to determine the current state of health of the system. Such health monitoring systems are common for bridge structures and many examples are citied in (Maalej et al., 2002). The phenomenon of damage in structures includes localized softening or cracks in a certain neighborhood of a structural component due to high operational loads, or the presence of flaws due to manufacturing defects. Damage detection component of health monitoring system are useful for non-destructive evaluations that are typically employed in agile manufacturing systems for quality control and structures, such as turbine blades, suspension bridges, skyscrapers, aircraft structures, and various structures deployed in space for which structural integrity is of paramount concern (Figure 1). With the increasing demand for safety and reliability of aerospace, mechanical and civilian structures damage detection techniques become critical to reliable prediction of damage in these structural systems. Most currently used damage detection methods are manual such as tap test, visual or specially localized measurement techniques (Doherty, 1997). These techniques require that the location of the damage have to be on the surface of the structure. In addition, location of the damage has to be known a priori and these locations have to be readily accessible. This makes current maintenance procedure of large structural systems very time consuming and expensive due to its heavy reliance on human labor.

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