Deep Learning for Surface Assessment of Cylinder Liners in Large Internal Combustion Engines

Abstract

Abstract As digitalization advances, improvements in technology have made it easier to conduct condition assessment of key components of large internal combustion engines such as cylinder liners. Due to their movement relative to the piston, the inner surfaces of the liners are subject to constant wear, and it is critical that the engine operator is informed in advance of any imminent damage. This study deals with wear assessment for cylinder liners from Type 6 gas engines from INNIO Jenbacher GmbH & Co OG with a cylinder displacement of approximately 6 dm3. Currently, wear quantification with this type of liner requires high-resolution microscopic surface depth measurements. The depth maps of the surface are then used for further analysis of the liner surface topography. To perform these microscopic measurements, the liners must be disassembled, cleaned and cut into segments, which is a major drawback of the current measurement process. Since the cylinder liners examined can no longer be used even if no major wear is detected, the main goal of the research presented here is to develop a method that is able to predict the depth map of a liner from a single RGB reflection image, i.e., a color image with no direct depth information. In recent years, depth map prediction from RGB images has become a vital part of image analysis in various fields such as the automotive industry, gaming and robotics. However, only a few studies deal with depth map predictions on a microscopic scale. For this study, both RGB and depth images of the cylinder liner surface with pixel-wise alignment are obtained with the help of the same confocal microscope. This data set contains 740 pairs of high-resolution microscopic depth and RGB reflection images capturing a roughly 2 × 2 mm area. As there are no landmarks, the depth of the surface is measured relative to the core of the profile. This is a main difference to most other studies, which mainly focus on absolute depth measurements. First, the physical connection between the depth and the reflection images is investigated and described mathematically. This theoretical model provides good insight into how the information about the structure of the surface contained in the RGB image can be separated from other influencing factors such as lighting condition or color. Next, a deep learning framework is proposed to estimate the liner depth profiles from the RGB reflection images. A convolutional neural network is trained in a supervised manner to learn the correspondence between RGB and depth images. Using the physical model obtained in the first step, an RGB image is reconstructed from the predicted depth map. To ensure the physical plausibility of the model’s predictions, the similarity between the RGB input and the corresponding reconstruction is enforced by a reconstruction term. The proposed machine learning approach is comprehensively evaluated using meaningful distance measures between depth predictions and corresponding ground truth profiles. The results show that the proposed method is able to predict the depth profiles of the cylinder liners very accurately, indicating the great potential for engine liner wear assessment using microscopic RGB images.