干扰条件下基于改进SOM故障诊断研究
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摘要
针对机载电子设备在噪声干扰条件下故障诊断效果不佳的问题,基于自组织特征映射网络(SOM),提出了改进的SOM的网络算法,该算法在标准SOM网络的基础上引入了滤波算法进行初级降噪处理,然后进行阈值学习,重新定义了邻域函数和学习率,最后以故障评价指标为基准进行故障的隔离定位。在高斯白噪声条件下以某型飞机前端接收机的故障数据为例建立诊断模型。通过聚类和网络训练等仿真测试实验得到了故障模式的分类和隔离。同时通过与其他方法的性能比较验证了SOM神经网络在高斯白噪声干扰条件下故障诊断中的有效性、准确性和鲁棒性。
Aiming at the problem of poor fault diagnosis of airborne electronic equipment under noise interference conditions,an improved self-organizing feature mapping(SOM)network algorithm is proposed based on SOM network.Based on the standard SOM network,the algorithm introduces the filtering algorithm for primary noise reduction,then performs threshold learning,redefines the neighborhood function and learning rate,and finally uses the fault evaluation index as the benchmark to isolate the fault.Under the condition of Gaussian white noise,the fault data of the front-end receiver of a certain aircraft is taken as an example to establish a diagnostic model.The classification and isolation of fault modes are obtained through simulation tests such as clustering and network training.At the same time,the effectiveness,accuracy and robustness of SOM neural network in fault diagnosis under Gaussian white noise interference conditions are verified by comparison with other methods.
Aiming at the problem of poor fault diagnosis of airborne electronic equipment under noise interference conditions,an improved self-organizing feature mapping(SOM)network algorithm is proposed based on SOM network.Based on the standard SOM network,the algorithm introduces the filtering algorithm for primary noise reduction,then performs threshold learning,redefines the neighborhood function and learning rate,and finally uses the fault evaluation index as the benchmark to isolate the fault.Under the condition of Gaussian white noise,the fault data of the front-end receiver of a certain aircraft is taken as an example to establish a diagnostic model.The classification and isolation of fault modes are obtained through simulation tests such as clustering and network training.At the same time,the effectiveness,accuracy and robustness of SOM neural network in fault diagnosis under Gaussian white noise interference conditions are verified by comparison with other methods.
引文
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[15]张彼德,欧健,孙才新,等.汽轮发电机多故障诊断的SOM神经网络方法[J].重庆大学学报(自然科学版),2005,28(2):36-38.
[2]秦继容.现代直流伺服控制技术及其系统设计[M].北京:机械工业出版社,2002.
[3]张德丰.MATLAB神经网络应用设计[M].北京:机械工业出版社,2009.
[4]翟旭平,孟田,杨兵兵.基于SOM-SVM频谱感知算法[J].电子测量技术,2016,39(10):76-80.
[5]WU L,SUN R P,CHENG J H.Application of RBF neural network in fault diagnosis of FOG SINS[C].International Conference on Control,Automation and Systems,2012,42(18):69-75.
[6]吴祎,王友仁,陈妤,等.基于BPNN的电力电子参数性故障诊断方法[J].电子测量与仪器学报,2012,26(5):458-462.
[7]冯小平,李鹏,杨绍全.通信对抗原理[M].西安:西安电子科技大学出版社,2009.
[8]唐朝京.现代通信原理[M].北京:电子工业出版社,2010:210-265.
[9]徐明远,邵玉斌.MATLAB仿真在通信与电子工程中的应用[M].西安:西安电子科技大学出版社,2010:50-112.
[10]KOHONEN T.Essentials of the self-organizing map[J].Neural Networks,2013(37):52-65.
[11]张彼德.汽轮发电机组振动多故障诊断模型及方法研究[D].重庆:重庆大学,2002:36-38.
[12]尤丽静,陈在平.基于自组织神经网络的轴流分机不对中故障诊断研究[J].天津理工大学学报,2010,26(1):99-102.
[13]WANG J S,LI S X,GAO J.SOM neural network fault diagnosis method of polymerization kettle equipment optimized by improved PSO algorithm[J].Scientific World Journal,2014, DOI:10.1155/2014/937680.
[14]肖治平,吴全文,曾洋.遗传BP网络在机载雷达故障诊断中的应用研究[J].计算机测量与控制,2011,19(1):14-16.
[15]张彼德,欧健,孙才新,等.汽轮发电机多故障诊断的SOM神经网络方法[J].重庆大学学报(自然科学版),2005,28(2):36-38.