潜艇舵机系统故障智能诊断技术研究

在潜艇舵机系统故障诊断问题的研究中,根据潜艇舵机系统的实际状况,分析其各组成部分发生故障的特点和原因建立故障树,采用层次分析法计算各故障原因的权重,形成相应的智能故障树.利用Access数据库建立故障诊断专家系统知识库,从智能故障树中提取一定的规则.系统采用宽度优先的正向推理和搜索策略的控制策略进行推理.针对传统故障诊断专家系统缺乏自学习能力的不足,采用BP神经网络算法进行修正.最后利用软件设计舵机系统故障诊断系统的开发平台和设计语言,可帮助维修人员更加方便快捷地对潜艇舵机系统的故障进行诊断和维修,降低了成本.     

基于故障树和双向联想记忆神经网络的桥式起重机故障诊断方法

针对桥式起重机的故障诊断系统,提出一种基于故障树(FTA)和双向联想记忆神经网络(BAM)相结合的故障诊断方法。通过FTA建立系统故障树,收集桥式起重机所有故障模式,进而归纳出BAM的学习样本,然后对样本学习联想,得到系统诊断结果。两种方法的结合,不但可完成单个故障的诊断,还可实现多种故障的综合处理,提高诊断能力。     

基于家庭网络技术的燃气壁挂炉故障监测系统设计

从燃气壁挂炉维修与维护的局限性出发,提出基于家庭网络技术的燃气壁挂炉故障监测系统。该系统保留传统燃气壁挂炉显示面板和控制主板的通讯接口,硬件上串接一个故障跟踪模块。在家庭网络下,故障跟踪模块过滤出燃气壁挂炉的实时状态信息,通过网络上传到远程服务器。远程协助维修员通过上传的实时状态信息,在线给予用户故障剔除方案。本设计实现了燃气壁挂炉的远程维修与维护,为后续的燃气壁挂炉故障诊断风险分析提供了可靠的依据。     

大型乙烯“三机”情况调研及分析

介绍了我国乙烯装置的现状,根据调研我国大型乙烯“三机”的情况,重点分析了乙烯“三机”存在的问题、原因及处理措施。     

Intelligent fault diagnosis for rolling bearings based on graph shift regularization with directed graphs

Graph shift regularization is a new and effective graph-based semi-supervised classification method, but its performance is closely related to the representation graphs. Since directed graphs can convey more information about the relationship between vertices than undirected graphs, an intelligent method called graph shift regularization with directed graphs (GSR-D) is presented for fault diagnosis of rolling bearings. For greatly improving the diagnosis performance of GSR-D, a directed and weighted k-nearest neighbor graph is first constructed by treating each sample (i.e., each vibration signal segment) as a vertex, in which the similarity between samples is measured by cosine distance instead of the commonly used Euclidean distance, and the edge weights are also defined by cosine distance instead of the commonly used heat kernel. Then, the labels of samples are considered as the graph signals indexed by the vertices of the representation graph. Finally, the states of unlabeled samples are predicted by finding a graph signal that has minimal total variation and satisfies the constraint given by labeled samples as much as possible. Experimental results indicate that GSR-D is better and more stable than the standard convolutional neural network and support vector machine in rolling bearing fault diagnosis, and GSR-D only has two tuning parameters with certain robustness.     

基于北斗卫星通信的航天测控频段故障诊断系统设计

针对传统航天测控频段故障诊断方法受到信息传输时延影响而导致诊断精准度低的问题,提出基于北斗卫星通信的航天测控频段故障诊断系统设计;硬件结构设计了判断故障类型的波头信号检测模块;通过地面接收机接收卫星发出的微弱信号,计算本机晶振脉冲数,以此修改北斗卫星通信数据;利用北斗授时模块,保证了输出时间的准确性,避免了延迟问题;软件部分用逻辑推理设计航天测控频段故障诊断流程,并使用北斗卫星诊断技术,纠正误差;由实验结果可知,该系统信号线松动脉冲信号诊断结果与实际情况一致,陀螺输出角度量漂移与实际值一致,误差为0,具有精准诊断结果.     

基于K均值与WPA-RBF神经网络模拟电路故障诊断

针对模拟电路故障诊断进行了研究,提出了一种新的方法。该方法包括haar的小波分解,对数据的归一化处理,以及用K均值优化RBF的中心向量和宽度,用狼群算法优化RBF的权值。首先用haar小波对所得的电路原始故障数据集进行变换,然后对变换后的数据进行归一化处理,最终得出RBF神经网络训练所需的输入数据。针对RBF神经网络中隐层节点中心、基函数宽度及权值选取困难问题,这里用K均值优化RBF的中心向量和宽度,用狼群算法优化RBF的权值,以提高网络训练稳定性与诊断成功率。最终通过两个电路的诊断实例,来论述该方法的具体实现过程,验证用该方法进行模拟电路故障诊断的可行性。