Context We are - as organisation - ordering, maintaining, modernizing and driving rolling stock on behalf of SBB Passenger Transport. The maintenance of the rolling stock is - generally spoken - based on planned and unplanned maintenance activities or tasks. Condition based maintenance or even predictive maintenance based on data acquisition and analysis is becoming more important. Content We provide a set of images taken from a roof top of a certain rolling stock fleet, using two high resolution cameras; what is of interes is the "condition" of the slider element of the catenary; we are willing to provide these images with the aim, that Image processing and analytics shall help SBB and other railway undertakers to manage their slide elements condition based. We managed in a proof of concept using tensorflow to identify "angled polish" and "damage" - even - measuring the thickness of the remaining slider element. Inspiration We are aware, that the data will be in front of the world's largest data science community. Therefore we are looking for alternative solutions to detect as much as possible of the condition of said slider. Maybe even from other parts which are shown in the images.