Automatic detection on wear features of aero-engine honeycomb sealing ring

Abstract

The wear conditions of honeycomb sealing rings in aerospace engines are often complex. Traditional human operations based on sample paste exhibit poor adaptability and are inefficient. This paper proposes an automated detection method for the geometric features of wear marks on honeycomb sealing structures based on depth ratio features. Adaptive identification and quantification of cellular wear through point cloud data analysis. First, the point cloud data is cropped, followed by a least-squares fit iterative method to compute a reference line at a specified cross-section, which serves as a standard for computing the width and depth of wear marks while denoising the point cloud data. Subsequently, N-neighborhood sets and the depth ratio features within these sets are introduced, transforming the task of detecting wear marks’ start and end points into a peak detection problem. An improved automatic multiscale-based peak detection (AMPD) algorithm with a masking mechanism is utilized to determine the extent of each wear mark. Finally, the geometric features are calculated for each wear mark. Experimental results demonstrate that the proposed method can robustly identify wear areas with varying depths and distributions, measurement time reduced by more than 90%, and fulfilling the requirements for identifying and measuring honeycomb wear marks.