飞机刹车盘动片损伤程度及更换需求智能评估算法

Intelligent evaluation algorithm for damage degree and replacement need of braking moving disc

飞机刹车盘动片损伤超标将直接威胁飞行安全。为解决人工经验的刹车盘动片损伤程度评估存在检测效率低、主观性强等问题,提出一种基于图像分割的飞机刹车盘动片损伤程度及更换需求智能评估算法。首先,分析刹车盘动片损伤分类及特征信息;然后,基于U-Net模型语义分割刹车盘动片损伤图像数据集,构建刹车盘动片损伤区域面积占比量化分析模型和脱落区域最大径向宽度计算模型;最后,使用Cessna525刹车盘动片图像数据验证模型。结果表明:U-Net模型对刹车盘动片损伤类型的图像分割效果良好,平均精确率、平均召回率、平均像素准确率与平均交并比(IoU)分别为90.75%、91.25%、90.25%与87.25%。刹车盘损伤程度评估结果与经验丰富的机务工程师评估结论基本一致,且评估结果客观准确、可视化程度高,证明所提算法的合理性和可行性。

Excessive damage to aircraft braking moving discs will directly threaten flight safety.In order to solve the problems of low detection efficiency and strong subjectivity in the evaluation of damage degree of braking moving disc based on manual experience,an intelligent evaluation algorithm for damage degree and replacement need of aircraft braking moving disc based on image segmentation was proposed.Firstly,the classification and feature information of braking moving disc damage were analyzed.Then,based on the U-Net model,semantic segmentation was performed on the braking moving disc damage image dataset.A quantitative analysis model for the proportion of braking moving disc damage area and a calculation model for the maximum radial width of the fall off area were constructed.Finally,the model was validated using image data of the braking moving disc of Cessna525.The results indicate that the U-Net model has a good image segmentation effect on the types of braking moving disc damage,with average accuracy,average recall,average pixel accuracy,and average intersection to union(IoU)of 90.75%,91.25%,90.25%,and 87.25%,respectively.The evaluation results of braking moving disc are basically consistent with the evaluation conclusions of experienced mechanical engineers,and the evaluation results are objective,accurate,and highly visualized,which proves the rationality and feasibility of the algorithm proposed in this paper.