大转数波动条件下机理约束图权重增强网络的航空发动机附件机匣故障诊断方法

doi:10.3901/JME.2024.12.126

机械工程学报 ›› 2024, Vol. 60 ›› Issue (12): 126-136.doi: 10.3901/JME.2024.12.126

• 特邀专栏：可解释可信AI驱动的智能监测与诊断 • 上一篇下一篇

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大转数波动条件下机理约束图权重增强网络的航空发动机附件机匣故障诊断方法

余晓霞¹, 汤宝平², 魏静², 张志刚¹

1. 重庆理工大学机械工程学院重庆 400054;
2. 重庆大学机械传动全国重点实验室重庆 400044

收稿日期:2023-09-06 修回日期:2024-03-12 出版日期:2024-06-20 发布日期:2024-08-23
作者简介:余晓霞(通信作者),男,1993年出生,博士,讲师。主要研究方向为旋转机械智能运维。E-mail:xiaoxia@cqut.edu.cn;汤宝平,男,1971年出生,教授,博士研究生导师。主要研究方向为无线传感器网络、机电装备安全服役与寿命预测、测试计量技术及仪器。E-mail:bptang@cqu.edu.cn;张志刚,男,1972年出生,教授,博士研究生导师。主要研究方向为动力传动系的开发和智能控制。E-mail:zhangzhigang@cqut.edu.cn
基金资助:
国家重点研发计划(2020YFB1709800)、国家自然科学基金(51775065)、重庆市高校创新研究群体(CXQT21027)、装备预研教育部联合基金、重庆英才计划包干制(cstc2021ycjh-bgzxm0261)、重庆理工大学科研启动基金(0119230961)和重庆理工大学国家自然科学基金培育(0119230874)资助项目。

Fault Diagnosis for Aero-engine Accessory Gearbox by Mechanism Constraint Graph Weight Enhancement Networks under the Large Revolution Fluctuations

YU Xiaoxia¹, TANG Baoping², WEI Jing², ZHANG Zhigang¹

1. College of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054;
2. The State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044

Received:2023-09-06 Revised:2024-03-12 Online:2024-06-20 Published:2024-08-23

摘要/Abstract

摘要： 针对现有深度学习模型在故障特征提取过程中容易受到大转数波动工况影响，导致航空发动机附件机匣故障诊断精度较低的问题，提出了一种机理约束图权重增强网络(Mechanism constraint graph weight enhancement networks, MCGWENet)的航空发动机附件机匣故障诊断方法。首先通过度量振动信号时域和频域特征的欧式距离约束图的邻接矩阵，将物理机理嵌入到所构建的图结构中；然后将振动信号的小波包分解结果作为节点特征；并结合所设计的邻接矩阵构造出可用于航空发动机附件机匣故障诊断的图；最后，通过所设计的图权重增强层抑制大转数波动对故障特征提取的影响，提升所提模型的故障诊断精度。试验结果表明，所提模型能够有效识别机匣故障，可用于航空发动机附件机匣健康管理。

关键词: 航空发动机附件机匣, 机理约束, 图权重增强网络, 大转数波动, 故障诊断

Abstract: Aiming at the problem that the existing deep learning models are easily affected by the large revolution fluctuation in the process of fault feature extraction, which leads to the low accuracy of aero-engine accessory gearbox fault diagnosis, a mechanism constraint graph weight enhancement networks (MCGWENet) is proposed for accessory gearbox fault diagnosis. First, the adjacency matrix of the graph is constrained by measuring the euclidean distance of the time and frequency domain characteristics of the vibration signal, and the physical mechanism is embedded into the constructed graph structure. Then the results of wavelet packet decomposition of vibration signals are used as node features; and the designed adjacency matrix is combined to construct a graph that can be used for the fault diagnosis of aero-engine accessory gearbox. Finally, the designed graph weight enhancement layer is used to suppress the influence of large revolution fluctuation conditions on fault feature extraction and improve the fault diagnosis accuracy of the proposed model. The experimental results show that the proposed method can effectively identify faults and can be used for health management of aero-engine accessory gearbox.

Key words: aero-engine accessory gearbox, mechanism constraint, graph weight enhancement network, large revolution fluctuation, fault diagnosis

中图分类号:

TH17

余晓霞, 汤宝平, 魏静, 张志刚. 大转数波动条件下机理约束图权重增强网络的航空发动机附件机匣故障诊断方法[J]. 机械工程学报, 2024, 60(12): 126-136.

YU Xiaoxia, TANG Baoping, WEI Jing, ZHANG Zhigang. Fault Diagnosis for Aero-engine Accessory Gearbox by Mechanism Constraint Graph Weight Enhancement Networks under the Large Revolution Fluctuations[J]. Journal of Mechanical Engineering, 2024, 60(12): 126-136.

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大转数波动条件下机理约束图权重增强网络的航空发动机附件机匣故障诊断方法

Fault Diagnosis for Aero-engine Accessory Gearbox by Mechanism Constraint Graph Weight Enhancement Networks under the Large Revolution Fluctuations

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