State-of-the-art methods for quantifying wear in cylinder liners of large internal combustion engines for stationary power generation require disassembly and cutting of the examined liner. This is followed by laboratory-based 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 nondestructive yet reliable methods for quantifying the surface topography. A novel machine learning framework is proposed that allows prediction of the bearing load curves representing the depth profiles from reflection RGB images of the liner surface. These images can be collected with a simple handheld microscope. A joint deep learning approach involving two neural network modules optimizes the prediction quality of surface roughness parameters as well. The network stack is trained using a custom-built database containing 422 perfectly aligned depth profile and reflection image pairs of liner surfaces of large gas engines. The observed success of the method suggests its great potential for on-site wear assessment of engines during service.
翻译:在用于固定发电的大型内燃机的气瓶衬里,用最先进的量化磨损的方法,将固定发电大型内燃机的内燃机磨损数量化,需要拆卸和切割所检查的衬里。接着是实验室的高分辨率显微表面深度测量,根据承载的负载曲线(又称Abbott-Firestone曲线)进行定量评估。这种参考方法具有破坏性、耗时和昂贵。这里的研究目标是开发非破坏性但可靠的表层地形定量化方法。提出了一个新的机器学习框架,以便能够从衬里表面的反射 RGB 图像中预测代表深度剖面的承载曲线。这些图像可以用简单的手持显微镜收集。涉及两个神经网络模块的联合深学习方法优化了地表粗糙参数的预测质量。网络堆是一个定制数据库,内有422个完全一致的深度剖面图和大型气发动机表面的反射镜。观察到的方法的成功表明它极有可能在服务期间对发动机进行现场磨损评估。