摘要
民机复合材料胶接挖补工作负荷对适航维修差错控制和维修人员绩效提高等具有重要意义。利用“眼动+脑电”对复合材料胶接挖补工作负荷进行研究,建立复合材料夹层结构挖补修理实验场景,采集损伤材料去除、补片材料制作、铺层与固化环境构建三个典型挖补工艺过程的眼动行为和脑电信号并计算工作绩效和基于NASA-TLX量表的工作负荷。通过相关性分析提取与工作负荷显著相关的眼动+脑电特征模式。基于该特征模式,采用BP神经网络和支持向量机方法构建复合材料胶接挖补工作负荷预测模型。研究结果表明,随着工作负荷水平上升,工作绩效呈下降趋势,注视时间、眼跳时间、眼跳次数、平均眼跳频率更高,注视热点与注视轨迹分布更散乱,theta、alpha、beta波段脑电功率谱密度更高,大脑额叶、颞叶区域脑电信号更强烈;眼动+脑电特征模式结合支持向量机方法预测的工作负荷具有更高精度,可有效支持民机复合材料适航维修过程监测。
Workload is an important influencing factor of airworthiness maintenance errors control and personnel performance improvement in the bonding repair of civil aircraft composite materials.To study the workload,the eye movement and EEG(electrooculogram) generated in the repairing process were used.Firstly,the experimental scene of composite sandwich structure repair was established.Then,the eye movement behaviors and EEG signals were collected in three typical repairing tasks of “damage materials removal”,“scarf patch materials preparation” and “layering and curing environment construction” and the work performance and workload based on NASA-TLX were calculated.Finally,the feature pattern of “eye movement + EEG” which was significantly related to the workload was extracted by correlation analysis.Based on this pattern,BP neural network and support vector machine method were used to construct the workload prediction model.The results show that with the increase of workload,the level of work performance declines,the fixation time,saccade time,saccade frequency and average saccade frequency are higher,the distribution of fixation hotspots and trajectories are more scattered,the spectral density of EEG power in theta,alpha and beta bands are higher,and the EEG signals in the frontal and temporal lobes of the brain are stronger.The workload predicting model of “eye movement + EEG” feature mode combined with support vector machine method has higher accuracy,which can support the airworthiness maintenance process monitoring of civil aircraft composite materials effectively.
作者
贺强
胥涛
HE Qiang;XU Tao(College of Aviation Engineering,Civil Aviation Flight University of China,Guanghan 618307,China)
出处
《科学技术与工程》
北大核心
2024年第26期11449-11456,共8页
Science Technology and Engineering
基金
四川省科技计划资助应用基础研究项目(2021YJ0537)
民航飞行技术与飞行安全重点实验室自主研究项目(FZ2022ZZ01)。
关键词
复合材料
适航维修
胶接挖补
工作负荷
眼动
脑电
composite materials
airworthiness maintenance
bonding repair
workload
eye movement
EEG(electrooculogram)
