Fault diagnosis based on pulse coupled neural network and probability neural network
Fault diagnosis based on pulse coupled neural network and probability neural network
复制标题
基于脉冲耦合神经网络和概率神经网络的故障诊断
DOI：
10.1016/j.eswa.2011.05.095
复制
发表时间：
2011-10-01
影响因子：
8.5
通讯作者：
Zhang, Yongchuan
中科院分区：
文献类型：
--
作者：
Wang, Changqing;Zhou, Jianzhong;Zhang, Yongchuan
在机械设备的运行中，故障诊断发挥着重要作用。提出了一种基于脉冲耦合神经网络（PCNN）和概率神经网络（PNN）的故障诊断方法。轴心轨迹的形状信息为故障诊断提供了重要依据。然而，轴心轨迹的特征提取和分类很难实现自动化。PCNN技术在特征提取方面具有优良的性能。由于PCNN的时间信号具有对旋转、缩放和平移不敏感的特性，本文采用结合圆度法的PCNN提取轴心轨迹的特征向量。同时，圆度也具有相同的性质。进一步利用概率神经网络训练特征向量，对振动故障进行分类。通过与BP网络和径向基函数（RBF）网络的比较，实验结果表明该方法能够实现快速有效的故障诊断。(C)2011爱思唯尔有限公司版权所有。
In operation of mechanical equipment, fault diagnosis plays an important role. In this paper, a novel fault diagnosis method based on pulse coupled neural network (PCNN) and probability neural network (PNN) is presented. The shape information of shaft orbit provides an important basis for fault diagnosis. However, the feature extraction and classification of shaft orbit is difficult to realize automation. The PCNN technique has excellent performance in the feature extraction. In the present study, a PCNN combined with roundness method is used to extract the feature vector of shaft orbit, because time signature from a PCNN has the property of insensitive to rotation, scaling and translation. Meanwhile, roundness is also with the same properties. Further, the PNN is used to train the feature vectors and classify the vibration fault. By comparison with the back-propagation (BP) network and radial-basic function (RBF) network, the experimental result indicated the proposed approach achieved fast and efficient fault diagnosis. (C) 2011 Elsevier Ltd. All rights reserved.