  State-of-the-art methods for quantifying wear in cylinder liners of large
internal combustion engines require disassembly and cutting of the liner. This
is followed by laboratory-based high-resolution microscopic surface depth
measurement that quantitatively evaluates wear based on bearing load curves
(Abbott-Firestone curves). Such methods are destructive, time-consuming and
costly. The goal of the research presented is to develop nondestructive yet
reliable methods for quantifying the surface topography. A novel machine
learning framework is proposed that allows prediction of the bearing load
curves from RGB images of the liner surface that can be collected with a
handheld microscope. A joint deep learning approach involving two neural
network modules optimizes the prediction quality of surface roughness
parameters as well and is trained using a custom-built database containing 422
aligned depth profile and reflection image pairs of liner surfaces. The
observed success suggests its great potential for on-site wear assessment of
engines during service.
