Aero-engine pipe gap automatic detection based on 3D scanning point clouds

Abstract

Complex pipe systems are an important part of aero-engines to ensure reliable operations. These pipes are typically distributed in a narrow space while retaining a safe distance from each other. Thus, the gap between pipes needs to be periodically measured. Due to the complex structure of aero-engine pipes, there is no current effective method to detect pipe gaps. This paper proposes an automatic detection method for aero-engine pipe gaps based on raw point clouds. First, an iterative random point method is presented to obtain the center point set by relying on the geometric characteristics of the pipe. However, due to the complex background, the center point set contains significant noise and outliers. The key observation shows that the point density along a center curve and the mass of this curve are higher. We propose a mass and density-aware curve fitting algorithm capable of extracting the aero-engine pipe center curves with topological relationships and geometric details. We formulate the aero-engine pipe gap as the optimization model based on the distances between the center curves. Various experiments demonstrate the effectiveness and efficiency of the proposed approach, which exhibits superiority over several state-of-the-art methods in terms of the robustness to clutter and occlusion.