This week, October 30, 12:30-13:30h, in Pav. K16, we have our cluster meeting. Paulo will give a talk on “Remaining Useful Lifetime (RUL) prediction across different domains”. Below you can find the abstract of the talk.
Remaining Useful Lifetime (RUL) prediction across different domains:
X-ray tubes are critical components in scanners used for radiography procedures. Tubes are composed of tungsten filaments which are heated up to produce x-rays images. However, such filaments are subject to wear-out given intensive usage. Such behaviour can lead to an early failure of the filaments causing disruptions and incurring high maintenance costs. Currently, there are two types of scanner generators in operation; the newest generator can log enough sensor information for the construction of a Physics Model capable of estimating the RUL. However, older versions do not store crucial sensor data to enable a prediction. Inspired by this use case, we evaluate a transfer learning method to predict the RUL for an equipment when there is a shift in the input distribution among different data sources. We test the method on a benchmark data with different failure modes and unlogged RUL information.