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基于深度学习的航空发动机传感器故障检测
引用本文:刘云龙,谢寿生,郑晓飞,边涛.基于深度学习的航空发动机传感器故障检测[J].传感器与微系统,2017,36(9).
作者姓名:刘云龙  谢寿生  郑晓飞  边涛
作者单位:空军工程大学航空航天工程学院,陕西西安,710038
基金项目:国家自然科学基金资助项目
摘    要:针对传统反向传播(BP)神经网络和支持向量机(SVM)存在的过拟合、维数灾难、参数选择困难等问题,提出了一种基于深度学习算法的航空发动机传感器故障检测方法.对发动机参数记录仪采集的多维数据进行预处理,建立基于深度置信网络(DBN)的故障检测模型,利用预处理后的数据对检测模型进行训练,经过DBN故障检测模型逐层特征学习实现了传感器故障检测.仿真结果表明:在无人工特征提取和人工特征提取的情况下,基于DBN故障检测的准确率均高于BP神经网络和SVM模型.

关 键 词:航空发动机传感器  故障检测  深度学习  深度置信网络  飞参数据

Fault diagnosis of aero-engine sensor based on deep learning
LIU Yun-long,XIE Shou-sheng,ZHENG Xiao-fei,BIAN Tao.Fault diagnosis of aero-engine sensor based on deep learning[J].Transducer and Microsystem Technology,2017,36(9).
Authors:LIU Yun-long  XIE Shou-sheng  ZHENG Xiao-fei  BIAN Tao
Abstract:Aiming at the problems of traditional back propogation (BP) neural network and support vector machine (SVM) learning algorithm,such as over fitting,dimension disaster and difficulty of parameter selection,put forward an aircraft engine sensor fault detection method learning algorithm based on deep learning algorithm.Preprocess the multi dimensional data acquired by aero-engine parameter recorder;fault detection model based on the deep belief network(DBN) is set up;fault detection model is trained using proprocessed data,after DBN fault detection model characteristics learning layer by layer,sensor fault detection is realized.It is shown from the simulation results,in the absence of artificial feature extraction and feature extraction,accuracy based on DBN fault detection is higher than that of BP neural network and SVM model.
Keywords:aero-engine sensor  fault detection  deep learning  deep belief network(DBN)  flight parameter
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