基于GRU和改进注意力机制的多信息融合的EMA故障诊断方法被引量：1

Fault diagnosis method for EMA based on multi-source signal fusion with GRU and improved attention mechanism

在线阅读下载PDF

导出

摘要针对传统基于机器学习和深度学习的机电伺服系统(EMA)故障诊断方法存在时序特征丢失、故障信息丢失的问题,提出一种基于门控循环单元(GRU)和改进注意力机制的多信息融合的EMA故障诊断方法。将采集的不同传感器信号分为不同通道,通过GRU提取每个通道信号的时序特征,再引入自注意力机制进一步分辨信号不同时间点之间的重要关系,进一步引入多通道注意力机制自适应融合不同通道的特征,通过分类器实现故障诊断。基于测试试验台数据集的试验结果表明:所提方法与单传感器的模型相比,诊断准确率提升10%;与不引入注意力机制的模型相比,诊断准确率提升5.2%;与经典的机器学习、深度学习和近两年基于深度学习的改进算法相比,所提方法的诊断准确率在98.5%以上,诊断效果最优。 Addressing the issues of insufficient time-series features and incomplete fault information in fault diagnosis methods for electromechanical actuators(EMAs)based on traditional machine learning and deep learning,a fault diagnosis method for EMAs based on multi-source signal fusion with gated recurrent unit(GRU)and an improved attention mechanism is proposed.First,the collected signals from different sensors are divided into separate channels,and the time-series features of each channel’s signal are extracted using GRU.The self-attention mechanism is then introduced to further distinguish the important relationships between different time points of the signal.A multi-channel attention mechanism is employed to adaptively fuse the features from different channels.Finally,fault diagnosis is achieved through the classifier.Experimental results based on the test rig dataset show that the diagnostic accuracy improves by 10%compared to the single-sensor model and by 5.2%compared to the model without the attention mechanism.Compared to classical machine learning,deep learning and recent improvements in deep learning-based algorithms from the past two years,the diagnostic accuracy of the proposed model exceeds 98.5%,demonstrating optimal diagnostic performance.

作者彭朝琴李奇聪陈娟马纪明 PENG Zhaoqin;LI Qicong;CHEN Juan;MA Jiming(School of Automation Science and Electrical Engineering,Beihang University,Beijing 100191,China;School of Mechanical Engineering&Automation,Beihang University,Beijing 100191,China;Sino-French Engineer School,Beihang University,Beijing 100191,China)

机构地区北京航空航天大学自动化科学与电气工程学院北京航空航天大学机械工程与自动化学院北京航空航天大学中法工程师学院

出处《北京航空航天大学学报》北大核心 2025年第11期3734-3744,共11页 Journal of Beijing University of Aeronautics and Astronautics

基金国家自然科学基金(62373029)。

关键词门控循环单元多源信息融合自注意力机制通道注意力机制机电伺服系统故障诊断 gated recurrent unit multi-source signal fusion self-attention mechanism channel attention mechanism electromechanical actuators fault diagnosis

分类号 TH133.3 [机械工程—机械制造及自动化]

作者简介通信作者:彭朝琴,E-mail:pengzhaoqin@buaa.edu.cn。