Search Results

Every time we measure the terrain profiles we would get a different set of data due to the measuring errors and due to the fact that the linear tracks on which the measuring vehicle travels can not be exactly the same every time. However the data collected at different times from the same terrain should share the similar intrinsic properties. Hence it is natural to consider statistical modeling of the terrain profiles. In this paper we shall use the time series models with time being the distance from the starting point. We receive data from the Belgian Block and the Perryman3 testing tracks. The Belgian Block data are shown to behave like a uniformly modulated process([7]), i.e. it is the product of a deterministic function and a stationary process. The modeling of the profiles can be done by estimating the deterministic function and fit the stationary process with a well-known ARMA model. The Perryman3 data are more irregular.

The use of composite materials in the automotive industry has become increasingly widespread. With this increase in use, techniques for non-destructive testing (NDT) have become more and more important. Various optical NDT inspective methods such as holography, moiré techniques, and shearography have been used for material testing. Among these methods, shearography appears to be most practical. Shearography has a simple optical setup due to its “self-referencing” system, and it is relatively insensitive against rigid-body motions. Measurements of displacement derivatives, and thus strain directly, rather than the displacement itself is achieved through this method. Therefore shearography detects defects in objects by correlating anomalies of strain which are usually easier than correlating the anomalies of the displacement itself, as in holography. To date shearography has shown potential as a NDT tool for identifying defects in small structures.