High-Dimensional Digital Twins
If you’ve ever done any sort of structural inspection, you know that we collect a LOT of data over the life-cycle of a system, whether it’s a bridge, a pipeline, or a ship. The problem is, we tend to look at each information source in a vacuum, and most of our analysis isn’t particularly quantitative. What we propose it to integrate all of this data into a digital twin of a given system. A digital twin is a living model, one that can be used for numerical simulations, visualizations, or just complex data management. Inspection data gets layered onto this model and then analyzed.
Things start getting interesting once you’ve formed the model. We’re exploring methods of mathematically fusing these data together, using a combination of pattern extraction and high-dimensional machine learning techniques. Basically, for each data type, we start by describing critical data (say, a flaw) as a set of numerical parameters, sometimes referred to as a feature vector. This helps to reduce data size and makes for more repeatable analyses. We’ve already done this for image and image-like information. If different observations of the same flaw exist, these can be stacked into one larger vector and then tracked through high-dimensional geometric analysis.
More to come!