基于振动信号的航空发动机盘轴裂纹智能预警方法

甘甜; 王琼; 王栋欢; 丁宝庆; 梁华; 李应红

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基于振动信号的航空发动机盘轴裂纹智能预警方法

更新时间：2026-09-23

- 基于振动信号的航空发动机盘轴裂纹智能预警方法
- A Vibration Signal-Based Intelligent Prediction Method for Aero-Engine Disk-Shaft Cracks
- 西安交通大学学报 2026年
- 作者机构：
  
  1.空军工程大学，710051，陕西西安
  2.动力与能源学院，西北工业大学，710072，陕西西安
  3.航空发动机研究院，西安交通大学，710115，陕西西安
- 作者简介：
- 基金信息：
- DOI：
  中图分类号： V263.6
- 收稿：2026-05-15，
  
  修回：2026-07-03，
  
  录用：2026-09-19，
- 稿件说明：
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甘甜, 王琼, 王栋欢, 等. 基于振动信号的航空发动机盘轴裂纹智能预警方法[J/OL]. 西安交通大学学报,2026-09-23. http://zkxb.xjtu.edu.cn/zh/article/172414237/.

GAN Tian, WANG Qiong, WANG Donghuan, et al. A Vibration Signal-Based Intelligent Prediction Method for Aero-Engine Disk-Shaft Cracks[J/OL]. JOURNAL OF XI’AN JIAOTONG UNIVERSITY,2026-09-23. http://zkxb.xjtu.edu.cn/zh/article/172414237/.
甘甜, 王琼, 王栋欢, 等. 基于振动信号的航空发动机盘轴裂纹智能预警方法[J/OL]. 西安交通大学学报,2026-09-23. http://zkxb.xjtu.edu.cn/zh/article/172414237/. DOI：

GAN Tian, WANG Qiong, WANG Donghuan, et al. A Vibration Signal-Based Intelligent Prediction Method for Aero-Engine Disk-Shaft Cracks[J/OL]. JOURNAL OF XI’AN JIAOTONG UNIVERSITY,2026-09-23. http://zkxb.xjtu.edu.cn/zh/article/172414237/. DOI：

摘要

为实现航空发动机盘轴裂纹的早期预警，提出基于物理嵌入网络架构的航空发动机裂纹故障预警方法。基于长短期记忆循环神经网络并结合发动机部件级物理拓扑结构，依据部件对输入参数进行聚类，搭建物理结构嵌入的神经网络架构。利用5台正常涡扇发动机搭建初始预警模型并进行模型预测性能评估，结果显示，模型在发动机N

转速40%~80%区间对振动的预测对称平均绝对百分比误差为6.07%，模型对发动机振动响应的实时预测可靠。采用滚动式更新策略构建动态预测模型并进行精度验证，基于振动值得趋势偏差分析，提前捕获了具有单向性、累积性、不可逆特征的异常特征，证实该方法的有效预警。该方法在不依赖历史故障数据、不增设额外传感器的条件下，实现了高精度、可解释、可验证的显著优势，为航空发动机结构健康监测从事后诊断向事前预警转型提供了可行的技术方案。

Abstract

To achieve early warning of disk-shaft cracks in aero-engines

a crack fault warning method based on a physics-embedded network architecture is proposed. A neural network architecture embedded with physical structure is constructed by integrating Long Short-Term Memory (LSTM) recurrent neural networks with the component-level physical topology of the engine

where input parameters are clustered according to engine components. An initial warning model is established using data from five healthy turbofan engines

and it

s predictive performance is evaluated. The results show that the model achieves a symmetric mean absolute percentage error (sMAPE) of 6.07% for vibration prediction in the engine N

speed range of 40%-80%

demonstrating reliable real-time prediction of engine vibration response. A dynamic prediction model is constructed using a rolling update strategy and validated for accuracy. Based on trend deviation analysis of vibration values

abnormal features characterized by unidirectionality

cumulativity

and irreversibility are captured in advance

confirming the effectiveness of the proposed early warning method. Without relying on historical fault data or additional sensors

this method offers significant advantages in terms of high accuracy

interpretability

and verifiability

providing a feasible technical pathway for transitioning aero-engine structural health monitoring from post-event diagnosis to pre-event warning.

关键词

Keywords

references

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