基于LSTM的风洞设备健康状态评估方法研究  

Study of Health Assessment for Wind Tunnel Based on LSTM

作　　者：吴魁 孙洁[1] 蒋波 王仙勇[1] 黄玉龙[1,2] Wu Kui;Sun Jie;Jiang Bo;Wang Xianyong;Huang Yulong(Beijing Aerospace Measure&Control Corp.Ltd,Beijing　100041,China;Beijing Key Laboratory of High-speed Transport Intelligent Diagnostic and Health Management,Beijing　100041,China)

机构地区：[1]北京航天测控技术有限公司,北京100041 [2]北京市高速交通工具智能诊断与健康管理重点实验室,北京100041

出　　处：《计算机测量与控制》2018年第3期288-291,共4页Computer Measurement &Control

摘　　要：针对风洞设备健康状态评估中特征提取困难、量化算法复杂等问题,提出了一种基于深度学习的健康度评估方法,利用正常状态样本数据训练LSTM编解码器网络并构建特征空间,利用测量数据特征向量与特征空间的欧氏距离衡量健康状态的退化程度,从而高效地实现了系统或设备的健康状态量化评估;经风洞试验室轴流风机转子不平衡故障、长轴轴承裂缝故障等两个数据集进行验证,取得了与设备工作状态一致的健康度评估值,具有很强的工程应用价值。This paper presents a health assessment method for wind tunnel based on deep learning networks,which utilizes the LSTM encoder-decoder to build up the normal status features space via computing the hidden status of examples data.Meanwhile,the quantitative value of the diversity between the measurement data and example data is calculated using Euclidean distance from feature vector to the normal status feature space,and then hundred-mark health index can be specified.As result,health assessment method achieves high accuracy in evaluation of two fault data sets of wind tunnel,which show a significant effect and strong application value.

关 键 词：深度学习 LSTM 健康状态评估 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]