改进的坐标残差网络应用于17-4PH材料蚀后剩余寿命预测研究

邸娟; 王程波; 贺磊; 牛亚龙; 彭超义; 王建锋

doi:10.7652/xjtuxb202501019

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改进的坐标残差网络应用于17-4PH材料蚀后剩余寿命预测研究

更新时间：2026-01-06

- 改进的坐标残差网络应用于17-4PH材料蚀后剩余寿命预测研究
- Improved CA-ResNet Network for Residual Life Prediction of 17-4PH Material Damaged by Cavitation Erosion
- 西安交通大学学报 2025年59卷第1期页码：206-214
- 作者机构：
  
  1.太原科技大学车辆与交通工程学院， 030024，太原
  2.株洲时代新材料科技股份有限公司， 412007，湖南株洲
  3.湖南大学材料科学与工程学院， 410082，长沙
  4.同济大学工程结构性能演化与控制教育部重点实验室， 200092，上海
- 作者简介：
  
  邸娟（1988—），女，副教授，硕士生导师。
- 基金信息：
  
  国家自然科学基金青年科学基金资助项目(52205212);山西省基础研究计划青年科学研究资助项目(20210302124052)
- DOI：10.7652/xjtuxb202501019
  中图分类号： TK405
- 收稿：2024-05-29，
  
  网络首发：2024-07-19，
  
  纸质出版：2025-01-10
- 稿件说明：
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邸娟, 王程波, 贺磊, 等. 改进的坐标残差网络应用于17-4PH材料蚀后剩余寿命预测研究[J]. 西安交通大学学报, 2025,59(1):206-214.

DI Juan, WANG Chengbo, HE Lei, et al. Improved CA-ResNet Network for Residual Life Prediction of 17-4PH Material Damaged by Cavitation Erosion[J]. Journal of Xi’an Jiaotong University, 2025, 59(1): 206-214.
邸娟, 王程波, 贺磊, 等. 改进的坐标残差网络应用于17-4PH材料蚀后剩余寿命预测研究[J]. 西安交通大学学报, 2025,59(1):206-214. DOI： 10.7652/xjtuxb202501019.

DI Juan, WANG Chengbo, HE Lei, et al. Improved CA-ResNet Network for Residual Life Prediction of 17-4PH Material Damaged by Cavitation Erosion[J]. Journal of Xi’an Jiaotong University, 2025, 59(1): 206-214. DOI： 10.7652/xjtuxb202501019.

摘要

为解决材料汽蚀损伤后剩余使用寿命预测困难的科学问题，提出了一种融合卷积神经网络技术的寿命预测方法，具体采用残差网络（ResNet）模型，并嵌入坐标注意力机制（CA），通过对模型进行卷积、通道数及下采样方式的优化，构建改进的坐标残差网络（CA-ResNet）模型，以实现对17-4PH材料汽蚀损伤后剩余使用寿命的精确预测。基于超声波汽蚀试验得到的汽蚀特性曲线，通过逻辑回归（Logistic）方程对汽蚀阶段进行定量划分，并定义寿命系数

，同时借助超景深显微镜获取材料损伤后不同时刻的显微图像数据库，并与寿命系数

相对应。研究结果表明，改进的CA-ResNet网络模型在CIFAR10公开数据集上验证准确率可达92.2%，在收集的17-4PH材料的汽蚀损伤数据集上的验证准确率可达93.2%，相比ResNet18网络模型，准确率分别提高了1.5%和3.5%。通过学习率、批处理量等超参数优化后，该模型在汽蚀损伤数据集上准确率可达95.0%。采用端到端的数据驱动思想，可实现从汽蚀损伤形貌到汽蚀寿命的精确预测。

Abstract

In order to address the scientific challenge of predicting the remaining useful life of materials damaged by cavitation erosion

a life prediction method that integrates convolutional neural network technology is introduced. Specifically

the residual network (ResNet) model is utilized and enhanced with a coordinate attention mechanism (CA). Through optimization of the convolution

channel number and down-sampling method in the model

an improved coordinate residual network (CA-ResNet) model is developed to accurately predict the remaining useful life of 17-4PH material damaged by cavitation erosion. Initially

the cavitation characteristic curve is obtained from ultrasonic cavitation tests. Then

the cavitation stages are quantitatively segmented using a Logistic equation

defining the life coefficient

. Meanwhile

with the assistance of a super-depth-of-field microscope

microscopic images of the material at various post-damage time points are captured to establish a microscopic image database correlated with the life coefficient

. The results demonstrate that the improved CA-ResNet network model achieves a verification accuracy of 92.2% on the CIFAR10 public dataset and 93.2% on the collected cavitation damage dataset of 17-4PH material. This represents a 1.5% and 3.5% accuracy improvement over the ResNet18 network model

respectively. By fine-tuning hyperparameters like learn

ing rate and batch size

the accuracy on the cavitation damage dataset is elevated to 95.0%. In this paper

an end-to-end data-driven approach is adopted to achieve accurate prediction from cavitation damage morphology to post-cavitation life.

关键词

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references

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