Fatigue Damage estimation in vehicle from minimal instrumentation thanks to a mixed Engineering / Data Science approach

Abstract

Monitoring damage and its potential causes in lab-tested structures requires extensive instrumentation that cannot be feasibly replicated in production assets. Since strain gauge instrumentation at a large scale is impractical, other proxy measurements for damage such as pressure, temperature or acceleration can be monitored and damage can be inferred from them. There is considerable difficulty in understanding not only which events lead to significant damage accumulation, but also in suitably and economically instrumenting assets in order to capture these data.We propose a general framework for exploring data which is inspired from the field of data science. Using a combination of engineering calculation packages and open-source data science tools, we show how engineers can further their understanding of the problem domain. We will present two case studies, each involving a different automotive component that is extensively instrumented with strain gauges, accelerometers and other sensors. We evaluate absolute damage from the strain data and, by using engineering indicators, data reduction, visualisations, correlations, we show how a minimal instrumentation subset can be identified for the purpose of damage approximation.