Data Mining for Damage Detection in Engineering Structures

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. Methods that detect damage in the structure are useful for non-destructive evaluations that typically are employed in agile manufacturing and rapid prototyping systems. In addition, there are a number of 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. The 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 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.