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.     

二维直线运动平台几何误差建模与数字化分析

针对二维直线运动平台的定位精度受到绕X、Y、Z三个方向转动产生的角位移误差和沿着这三个方向的线位移误差的影响,采用一种基于三维模型的数字化方法对这些几何误差进行分析。通过参考坐标系和齐次变换矩阵建立二维直线运动平台几何误差运动学模型,并基于误差模型对运动平台的各项几何误差进行分配、计算和合成。基于以上误差分配原则对运动平台使用的导轨提出形位公差要求。通过上述工作,在虚拟样机设计阶段能对所设计结构几何误差进行预测,为后续精密运动的设计提供可靠的参考。     

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。     

基于BP神经网络的液压系统故障诊断专家系统

本文尝试将BP神经网络应用于液压系统的故障诊断，阐述了基于BP神经网络的故障诊断专家系统的基本结构，以及知识库，正向推理机和解释机的实现方法。     

Fault diagnosis of internal combustion engines using visual dot patterns of acoustic and vibration signals

An investigation of the fault diagnosis technique in internal combustion engines based on the visual dot pattern of acoustic and vibration signals is presented in this paper. Acoustic emissions and vibration signals are well known as being able to be used for monitoring the conditions of rotating machineries. Most of the conventional methods for fault diagnosis using acoustic and vibration signals are primarily based on observing the amplitude differences in the time or frequency domain. Unfortunately, the signals caused by damaged elements, such as those buried in broadband background noise or from smearing problems arising in practical applications, particularly at low revolution, are not always available. In the present study, a visual dot pattern technique is proposed to identify the acoustic emission and vibration signals for fault diagnosis in an internal combustion engine and drive axle shaft. Experiments are carried out to evaluate the proposed system for fault diagnosis under various fault conditions. The experimental results indicate that the proposed technique is effective in the fault diagnosis of an internal combustion engine and drive axle shaft.     

Bearing failure detection using matching pursuit

In this paper, a new approach to the detection of localized defects of rolling element bearings is proposed. It employs matching pursuit with time–frequency atoms to analyze bearing vibration and extract vibration signatures. In particular, this approach utilizes not only the temporal and spectral but also the scale characteristics of the vibration generated due to the presence of a defect for the detection. This leads to a high signal-to-noise ratio and facilitates considerably the detection at the early stage of failure development. Experimental results show that the proposed approach is sensitive and reliable and works better than continuous wavelet transform and envelope detection.     

基于容积效率的液压齿轮泵状态监测与故障诊断

阐述了容积效率作为特征信息进行液压齿轮泵状态监测与故障诊断的原理,分析了液压齿轮泵中流量信号以及容积效率与齿轮泵工况之间的关系,指出了容积效率是状态信息的丰富载体,讨论了利用容积效率进行液压齿轮泵状态监测与故障诊断的可行性及方法.