The increasing ability to acquire high quality road data in high volumes has introduced the complexity of analyzing them using the traditional predictive reliability techniques. Modern engineering tools for data science are therefore required to shorten the analysis times, making it more efficient and robust.
Valeo has recently collaborated with HBM Prenscia to implement nCodeDS, a new engineering-focused solution for data science available in Aqira. This presentation explores how Valeo has taken ""Big Road Load Data"" and achieved efficient post-processing of data, detected trends inside this data, and built robust predictive models for making reliability predictions.
A case study will investigate how this approach has been used to predict in-service fatigue damage for a new range of heat exchangers.
Originally presented on September 9, 2020