Event Detection Methods for Multi-Sensor CAE-Data

Virtual product development especially in car development requires the evaluation of multiple sensors signals in the simulations as one of the tasks; the sensor data is also needed for comparison with the real product. Comparing many virtual sensors manually from many simulations turns out to be a time consuming and challenging task. We propose a methodology and workflow setting that address this challenge, allowing a similarity comparison of hundreds of sensors from hundreds of simulations detecting similar events (clusters) or very different behavior as outliers. The approach uses a method of dimensionality reduction combined with different type of clustering methods including hierarchical clustering. The dimensionality reduction reduces the virtual sensor data information such that a visual comparison of thousand sensor signals can easily be performed in 3D, the hierarchical clustering on the other hand allows a localized comparison of sensor signals. The approach is demonstrated using binout Ls-Dyna data from a frontal crash example with many model variants containing many sensor data per simulation as well as for head impact computation.