最大化聚合注意力卷积胶囊网络：一种电静液作动器复合故障智能解耦诊断方法

doi:10.3901/JME.260131

机械工程学报 ›› 2026, Vol. 62 ›› Issue (4): 355-365.doi: 10.3901/JME.260131

• 交叉与前沿 • 上一篇

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最大化聚合注意力卷积胶囊网络：一种电静液作动器复合故障智能解耦诊断方法

赵孝礼¹, 胡渊豪¹, 孙辉², 邓文翔¹, 胡健¹, 姚建勇¹, 李杨³, 邵海东⁴

1. 南京理工大学机械工程学院南京 210094;
2. 江苏汇智高端工程机械创新中心有限公司徐州 221000;
3. 西南交通大学智慧城市与交通学院成都 611756;
4. 湖南大学机械与运载工程学院长沙 410082

收稿日期:2025-02-14 修回日期:2025-08-05 发布日期:2026-04-02
作者简介:赵孝礼,男,1991年出生,博士,副教授。主要研究方向为机电液装备故障诊断与智能运维、人工智能与数字孪生等。E-mail:xlzhao@njust.edu.cn
姚建勇(通信作者),男,1984年出生,博士,教授,博士研究生导师。主要研究方向为机电液伺服控制,故障检测与容错控制,智能机器人等。E-mail:yaojianyong@njust.edu.cn
基金资助:
国家自然科学基金资助项目(52175066)。

Maximization Aggregation Attention Convolutional Capsule Network: Intelligent Decoupling Diagnosis Method for Compound Faults in Electro-hydrostatic Actuators

ZHAO Xiaoli¹, HU Yuanhao¹, SUN Hui², DENG Wenxiang¹, HU Jian¹, YAO Jianyong¹, LI Yang³, SHAO Haidong⁴

1. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094;
2. Jiangsu Advanced Construction Machinery Innovation Center Ltd., Xuzhou 221000;
3. School of Institute of Smart City and Intelligent Transportation, Southwest Jiaotong University, Chengdu 611756;
4. College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082

Received:2025-02-14 Revised:2025-08-05 Published:2026-04-02

摘要/Abstract

摘要： 电静液作动器(Electro-hydrostatic actuator，EHA)作为一种复杂的机、电、液综合系统，在航空航天等领域具有重要作用。然而，由于其内部结构复杂、工作环境恶劣等因素，复合故障是其最为常见的故障形式之一，不仅具有隐蔽性和耦合性，还具有不确定性和因果关系复杂性，往往被误认为是单一故障从而导致漏警或误判。考虑到液压系统复合故障数据采集成本高昂、单一传感器信息感知有限及故障特征不明显等问题，提出一种基于最大化聚合注意力卷积胶囊网络(Maximizing aggregation attention convolutional capsule network，MAACCN)的电静液作动器复合故障智能解耦诊断方法。该方法利用多维传感信息的特征级融合与深度特征提取，使模型在仅使用单一故障数据训练的情况下，即可准确识别并解耦复合故障。EHA故障数据集验证表明，所提出的方法达到了97.4%的子集准确率，且汉明损失仅为0.025。

关键词: 电静液作动器, 复合故障, 最大化聚合, 注意力卷积胶囊网络, 解耦分类

Abstract: Electro-hydrostatic actuators(EHAs), as complex mechatronic-hydraulic integrated systems, play a significant role in aerospace and related fields. However, due to their intricate internal structures and harsh working environments, compound faults are among the most common types of failures. These faults exhibit concealment, coupling effects, uncertainty, and complex causal relationships, often being mistaken for single faults, which can lead to missed or incorrect alarms. Considering the high cost of collecting data on compound faults in hydraulic systems, the limited perception of single-sensor information, and the weak fault characteristics, an intelligent compound fault decoupling diagnosis method for EHAs based on a maximized aggregation attention convolutional capsule network(MAACCN) is proposed . By leveraging feature-level fusion of multidimensional sensing information and deep feature extraction, the proposed model can accurately identify and decouple compound faults even when trained using only single-fault data. Verification using an EHA fault dataset shows that the proposed method achieves a subset accuracy of 97.4% with a Hamming loss as low as 0.025.

Key words: electro-hydrostatic actuator, composite fault, maximizing aggregation, attention convolutional capsule network, decoupling classification

中图分类号:

TG156

赵孝礼, 胡渊豪, 孙辉, 邓文翔, 胡健, 姚建勇, 李杨, 邵海东. 最大化聚合注意力卷积胶囊网络：一种电静液作动器复合故障智能解耦诊断方法[J]. 机械工程学报, 2026, 62(4): 355-365.

ZHAO Xiaoli, HU Yuanhao, SUN Hui, DENG Wenxiang, HU Jian, YAO Jianyong, LI Yang, SHAO Haidong. Maximization Aggregation Attention Convolutional Capsule Network: Intelligent Decoupling Diagnosis Method for Compound Faults in Electro-hydrostatic Actuators[J]. Journal of Mechanical Engineering, 2026, 62(4): 355-365.

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最大化聚合注意力卷积胶囊网络：一种电静液作动器复合故障智能解耦诊断方法

Maximization Aggregation Attention Convolutional Capsule Network: Intelligent Decoupling Diagnosis Method for Compound Faults in Electro-hydrostatic Actuators

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