Machine Learning for Nondestructive Wear Assessment in Large Internal Combustion Engines
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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.
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