  Digitalization offers a large number of promising tools for large internal
combustion engines such as condition monitoring or condition-based maintenance.
This includes the status evaluation of key engine components such as cylinder
liners, whose inner surfaces are subject to constant wear due to their movement
relative to the pistons. Existing state-of-the-art methods for quantifying wear
require disassembly and cutting of the examined liner followed by a
high-resolution microscopic surface depth measurement that quantitatively
evaluates wear based on bearing load curves (also known as Abbott-Firestone
curves). Such reference methods are destructive, time-consuming and costly. The
goal of the research presented here is to develop simpler and nondestructive
yet reliable and meaningful methods for evaluating wear condition. A
deep-learning framework is proposed that allows computation of the
surface-representing bearing load curves from reflection RGB images of the
liner surface that can be collected with a simple handheld device, without the
need to remove and destroy the investigated liner. For this purpose, a
convolutional neural network is trained to estimate the bearing load curve of
the corresponding depth profile, which in turn can be used for further wear
evaluation. Training of the network is performed using a custom-built database
containing depth profiles and reflection images of liner surfaces of large gas
engines. The results of the proposed method are visually examined and
quantified considering several probabilistic distance metrics and comparison of
roughness indicators between ground truth and model predictions. The observed
success of the proposed method suggests its great potential for quantitative
wear assessment on engines and service directly on site.
