机电工程技术2026，Vol.55Issue(4)：14-19,6.DOI:10.3969/j.issn.1009-9492.2026.04.003
基于水浸超声技术的涡轮叶片裂纹损伤检测方法研究
Research on Crack Damage Detection Method for Turbine Blades Based on Immersion Ultrasonic Technology
摘要
Abstract
To address the difficulty in detecting surface micro-crack damage on coated turbine blades of a certain aero-engine,a crack detection method is proposed based on a one-dimensional convolutional neural network(1D-CNN)and water immersion ultrasonic testing.By analyzing the characteristics of abnormal signals during detection,a novel defect detection network is constructed using a 1D-CNN architecture that incorporates all feature mappings,along with an adaptive defect feature extraction module tailored for water immersion ultrasonic signals of turbine blades.To overcome the scarcity of real damage samples in practical detection scenarios,a data augmentation approach is employed to generate synthetic samples,thereby improving the detection accuracy of the network model for turbine blade inspection.Specialized tooling for turbine blades is developed,and an experimental platform for water immersion ultrasonic testing is established to validate and optimize the proposed detection model.Experimental results demonstrate that the proposed method achieves a detection accuracy of 96.8％for turbine blade damage,outperforming conventional detection models.This provides a novel solution for detecting surface micro-cracks in coated turbine blades.关键词
水浸超声/涡轮叶片/深度学习/微裂纹损伤/无损检测Key words
immersion ultrasound/turbine blade/deep learning/microcrack damage/non-destructive testing分类
矿业与冶金引用本文复制引用
张浩喆,黄刘伟,冯萍,何喜,方雨婷,陈振华,卢超,蔡苏阳..基于水浸超声技术的涡轮叶片裂纹损伤检测方法研究[J].机电工程技术,2026,55(4):14-19,6.基金项目
国家自然科学基金(12464059) （12464059）
航发技术委托项目(HFDL-KYZ-JSZ-202308-40) （HFDL-KYZ-JSZ-202308-40）
南昌航空大学博士启动基金(EA202408166) （EA202408166）
江西省早期人才项目(20244BCE52109) （20244BCE52109）
