A reasoning diagram based method for fault diagnosis of railway point system

Railway Point System(RPS)is an important infrastructure in railway industry and its faults may have significant impacts on the safety and efficiency of train operations.For the fault diagnosis of RPS,most existing methods assume that sufficient samples of each failure mode are available,which may be unrealistic,especially for those modes of low occurrence frequency but with high risk.To address this issue,this work proposes a novel fault diagnosis method that only requires the power signals generated under normal RPS operations in the training stage.Specifically,the failure modes of RPS are distinguished through constructing a reasoning diagram,whose nodes are either binary logic problems or those that can be decomposed into the problems of the binary logic.Then,an unsupervised method for the signal segmentation and a fault detection method are combined to make decisions for each binary logic problem.Based on the results of decisions,the diagnostic rules are established to identify the failure modes.Finally,the data collected from multiple real-world RPSs are used for validation and the results demonstrate that the proposed method outperforms the benchmark in identifying the faults of RPSs.

A New Method for the Detections of Multiple Faults Using Binary Decision Diagrams

With the complexity of integrated circuits is continually increasing, a local defect in circuits may cause multiple faults. The behavior of a digital circuit with a multiple fault may significantly differ from that of a single fault. A new method for the detection of multiple faults in digital circuits is presented in this paper, the method is based on binary decision diagram （BDD）. First of all, the BDDs for the normal circuit and faulty circuit are built respectively. Secondly, a test BDD is obtained by the XOR operation of the BDDs corresponds to normal circuit and faulty circuit. In the test BDD, each input assignment that leads to the leaf node labeled 1 is a test vector of multiple faults. Therefore, the test set of multiple faults is generated by searching for the type of input assignments in the test BDD. Experimental results on some digital circuits show the feasibility of the approach presented in this paper.

Cultural Algorithm for Minimization of Binary Decision Diagram and Its Application in Crosstalk Fault Detection

The binary decision diagrams （BDDs） can give canonical representation to Boolean functions; they have wide applications in the design and verification of digital systems. A new method based on cultural algorithms for minimizing the size of BDDs is presented in this paper. First of all, the coding of an individual representing a BDDs is given, and the fitness of an individual is defined. The population is built by a set of the individuals. Second, the implementations based on cultural algorithms for the minimization of BDDs, i.e., the designs of belief space and population space, and the designs of acceptance function and influence function, are given in detail. Third, the fault detection approaches using BDDs for digital circuits are studied. A new method for the detection of crosstalk faults by using BDDs is presented. Experimental results on a number of digital circuits show that the BDDs with small number of nodes can be obtained by the method proposed in this paper, and all test vectors of a fault in digital circuits can also be produced.

GhostConv轻量级网络设计及故障诊断研究

提出一种GhostConv轻量级网络模型并将其用于故障诊断。GhostConv利用常规卷积生成一小部分特征图,然后在生成的特征图上进行多次特征提取来生成其余特征图,最大程度地节约了常规卷积中生成冗余特征图的成本,减少了模型参数,保证了模型的性能。采用连续小波变换对振动信号进行时频变换生成二维时频图,之后利用设计的GhostConv搭建轻量级网络模型进行故障诊断。采用凯斯西储大学轴承数据集进行验证,并与其他卷积结构网络模型进行参数量、计算量以及识别准确率的对比。实验结果表明,与其他模型相比,所使用的网络模型在参数量和计算量较少的条件下依旧有较高的识别精度,且具有较好的鲁棒性和泛化能力,具有一定的工程应用价值。

基于改进残差网络的抽油机故障诊断研究

针对抽油机故障诊断的传统图像识别方法识别率高但速率较慢,或训练速度适宜但识别率较低等问题,提出一种基于改进残差网络模型的示功图图像识别算法。改进策略包括替换模型第1层卷积核,由更小卷积核代替;改变残差模块排列顺序;将传统ResNet50(残差网络)模型的全连接层替换成径向基函数(RBF:Radial Basis Function)网络作为额外的分类器;采用数据增强方式对数据集进行扩充,并利用迁移学习在改进的ResNet50-RBF模型得到ImageNet上预训练好的权重参数后进行训练。实验结果表明,改进的模型在示功图识别中得到了98.86%的准确率,与其他网络相比,鲁棒性进一步加强,并且速率得到一定提升,为抽油机故障诊断提供了一定参考。

基于无监督学习的抽油机井示功图自动聚类与批量标注方法

为充分利用大量未标注样本、节约人力与时间,提出了基于无监督学习的抽油机井示功图自动聚类与批量标注方法。首先,将抽油机驴头往复运动产生的位移、载荷数据转化为示功图图片样本,其中,示功图的横坐标为位移,纵坐标为载荷;其次,加载在ImageNet上训练过的带有一系列权重参数、具有强特征提取能力的卷积神经网络模型;然后,去除该网络模型的全连接层,利用该网络模型提取示功图图片样本的特征;最后,利用k-means聚类算法对提取到的特征进行聚类分析,将具有相似特征的示功图聚到同一文件夹中。批量的对示功图聚类结果进行快速标注,从而形成抽油机井故障诊断的示功图样本集。实验随机搜集了100口抽油机井的20 000条示功图数据,结果表明,基于无监督学习的抽油机井示功图自动聚类与批量标注方法耗时短、准确率高,为示功图样本集标注提供了一种高效方法,对于充分挖掘油田大数据的应用价值具有示范意义。

CAUSALITY DIAGRAM BASED SAFETY ANALYSIS OF MICRO TURBOJET ENGINE

An improved safety analysis based on the causality diagram for the complex system of micro aero-engines is presented.The study is examined by using the causality diagram in analytical failure cases due to rupture or pentration in the receiver of micro turbojet engine casing,and the comparisons are also made with the results from the traditional fault tree analysis.Experimental results show two main advantages：（1）Quantitative analysis which is more reliable for the failure analysis in jet engines can be produced by the causality diagram analysis;（2）Graphical representation of causality diagram is easier to apply in real test cases and more effective for the safety assessment.

基于改进动态因果图的湿法冶金浸出过程故障诊断方法研究

针对现有湿法冶金工艺机制复杂和浸出工艺故障诊断方法存在精度较差等问题,研究了用改进的动态因果图诊断湿法冶金浸出工艺故障,主要通过二元区间数对动态因果图进行优化改进,并对条件概率上下限进行加权,实现湿法冶金浸出工艺故障诊断。结果表明:该故障诊断方法相较于常规方法,故障诊断准确率更高,能对浸出工艺的故障进行较准确的诊断。

频域特征提取联合双流CNN的轴承故障诊断方法

针对传统方法提取到的轴承各类故障特征混杂,部分类别故障不易区分的问题,设计一种双流卷积神经网络(Convolutional Neural Network,CNN)的故障诊断模型。首先将振动信号转换到频域,为减少低频微弱信号的干扰,过滤频域信号,设定滤波器通带和阻带衰减值以保证信号不失真,进而确定频率带宽,在该带宽值下求得信号振幅占比最大值的范围,同时以正常信号振幅平均值作为高频信号的阈值确定频率最高值。用上述参数分别作为滤波器的参数,过滤信号得到频谱信号和构建时频图像。将提取频谱信号和时频图作为模型两个通道的输入,在卷积层和池化层后加入特征融合层,通过加权融合的方式将两个通道的特征融合,使得各类故障特征区分度显著提高。实地搭建故障平台采集数据验证,实验结果表明,该方法能提取到每类故障独有的特征,轴承故障识别准确率达到98.95%。

基于多链路连锁故障图的电网脆弱线路分析

为实现迅速正确地进行脆弱线路辨识,提出一种基于新型指标建立多链路事故链模型,并生成连锁故障图的分析方案。基于复杂网络理论对模型进行计算和分析,以系统负荷损失率为依据对线路脆弱性进行衡量,最后以IEEE39节点系统为算例验证了该方法的有效性。该方法突破了传统方法事故链模型建立过程中单对单线路的限制,并有效地实现了脆弱线路的辨识。

一种故障树预处理和最小割集的嵌套求解方法

故障树分析(Fault Tree Analyze,FTA)是一种系统安全性分析方法。求解故障树最小割集(Minimum Cut Set,MCS)是FTA的重要环节,其方法主要包括基于布尔代数的算法和基于二元决策图(Binary Decision Diagram,BDD)的算法,在使用普通计算机分析大规模故障树时,现有方法存在工作内存不足和计算时间过久的问题。为了解决上述问题,针对国内某百万千瓦级大型压水堆风险模型,提出了一种基于布尔代数的故障树预处理和最小割集嵌套求解算法(Pretreat and Nested Minimum Cut-Set Algorithm,PNMCS)。该算法由三个模块组成:故障树化简、故障树剪枝、最小割集嵌套计算。在国内某大型压水堆风险模型和几种实际应用风险模型上的应用表明,本算法在求得正确结果的同时,有效解决了工作内存不足和计算时间过久的问题。

基于功图故障特征分析的液量计量方法研究

为解决现有原油产液量计量方法精度低、成本高、工艺流程复杂等问题,对一种基于功图故障种类与强弱特征和BP神经网络分析的组合计算油井产量方法进行研究。该方法通过马田系统操作从地面示功图中提取特征向量,并融合自适应算法,对示功图故障种类及强弱进行识别与区分;建立了产液量与不同故障类型及强弱之间的映射关系以此代替泵效,计算映射关系并建立BP神经网络以此来优化关系,得到基于故障种类与强弱的计量产量的模型。给出该计量模型的具体实现,并选择2020年的新疆公司某作业区50井的产量及示功图作为数据集进行计算验证。上述模型计算结果表明,计算的石油产量和实际的石油产量作对比,其平均相对误差为3.26%,平均绝对误差为0.5%,满足油田生产利益最大化。

基于时频谱图的配电网高阻接地故障智能识别方法

针对配电网中发生高阻接地故障(high impedance fault, HIF)进行特征提取时,人为构造的特征量或特征向量难以充分体现高阻接地故障区别于其他事件的特征问题,提出一种波形隐含特征提取方法.通过连续小波变换将零序电流转换为时频谱图,并分割为正方形切片,然后使用卷积神经网络辨识高阻接地故障特有的“齿”形特征.结果表明:文中所提方法能由波形中的高频分量得到可视化的时频谱图,省去了人为构造的特征量的步骤,避免了特征量难以准确表征HIF特征的问题,其可靠性在仿真及现场样本测试中得到了验证.

基于贝叶斯网络和仿真分析的矿山机电系统可靠性评估

为增加矿山机电系统的可靠性,及时发现并预防潜在故障,提高矿山生产效率、确保工人安全及设备正常运行,建立了故障信息数据诊断系统,收集矿山机电系统故障数据,同时构建了贝叶斯故障网络,并利用ITE结构将其转化为二元决策图,对机电系统故障进行定性和定量分析。结果显示,在不同挖掘软件上,研究构建模型对机电系统故障的诊断准确率均达98%以上。在对某矿山机电系统实际评估中,测得绝缘老化或损坏、过载或过热、电子元件故障、内部短路对系统故障影响重要度最大,约为0.972。该故障分析系统有效提高了机电系统故障诊断的准确性和效率,对底层故障节点进行了有效评估,可为同类型机电系统的故障诊断和可靠性评估提供参考。

基于小波时频图和量子遗传算法的车轮扁疤检测方法

针对地铁车轮扁疤在线诊断困难问题,提出了一种基于小波时频图、灰度投影法和量子遗传算法的智能在线车轮扁疤检测方法。首先对轮轨间的垂向振动信号进行小波时频分析,得到小波时频图,然后结合灰度投影法从时频图中提取车轮扁疤的特征;最后采用量子遗传算法对支持向量机进行优化以提高SVM的分类准确率。通过一个工程实例验证了该方法在识别车轮扁疤时的有效性。结果表明,该方法能够准确且快速地检测出对车轮扁疤,且所使用的分类模型的性能优于现有模型。

基于模糊集和二叉决策图的装配式建筑施工安全风险评估

针对装配式建筑施工的安全风险,采用故障树分析法,识别装配式建筑施工过程中的危险源,通过应用模糊集理论和基于二叉决策图(BDD)结构的方法,能够对各个基本事件的模糊发生概率进行定量评估,从而推导出安全风险概率并确定各个基本事件的相对重要性。通过遍历BDD结构,管理人员可以有效把控装配式建筑施工中的薄弱环节和关键影响因素,实现安全风险的定性和定量分析。实证证明,引入模糊集理论和二叉决策图,能够更好地处理装配式建筑施工安全风险中的模糊性和随机性,并利用程序编程快速得到装配式建筑施工安全关键因素,提高了分析效率。

基于鱼骨图方法的车辆休眠后异常唤醒故障分析

文章采用鱼骨图分析法对问题原理进行分析,通过在某车型车辆休眠后异常唤醒电气故障分析中的实际应用,进一步说明鱼骨图分析方法在排查车辆存在问题的优势,对车辆开发工作有着十分重要的意义。

高压线路断路器开断故障电流零点漂移问题分析

高压交流输电线路需要准确预测电流零点时刻,从而有效确定断路器开断动作时刻,使断路器在接近于过零点时刻开断故障电流。导致开断相故障电流零点相对于预期的稳态短路电流发生漂移有以下两个因素:一是当发生短路故障时,首次开断故障电流相后,后开断相的故障电流的衰减直流分量在瞬态过程中将叠加在交流分量上从而导致其零点漂移;另一个是三相电流具有不同的零点时刻,当断路器中的一相断开电弧熄灭时,整个三相电路的拓扑结构将发生变化,从而导致其他两相的零点漂移。研究结果表明,针对中性点接地系统及非接地系统,在各种短路故障条件下,首开断相断开后,次开断相的电流零点会向前移动,而末开断相的电流过零点会向后移动。

浅析混动车型低压线束CAN网络通信丢失一般故障问题

随着CAN总线系统在汽车控制领域的应用越来越广泛,CAN总线系统故障对汽车控制功能的实现有很大的影响。为了减少CAN总线系统故障的发生率,提高驾乘体验,文章首先对处理总线故障的总体思路进行阐述,然后总结出几种常用的CAN总线诊断工具并对其进行介绍,最后从故障现象、诊断分析以及排查方法等方面入手,对在实际项目工作中遇到的CAN总线故障进行详细描述和说明,以便可以极大提高CAN总线的故障诊断与排除效率。

Estimation of the current stress field and fault reactivation analysis in the Asmari reservoir, SW Iran

Knowing the current condition of the faults and fractures in a reservoir is crucial for production and injection activities.A good estimation of the fault reactivation potential in the current stress field is a useful tool for locating the appropriate spot to drill injection wells and to calculate the maximum sustainable pore pressure in enhanced oil recovery and geosequestration projects.In this study,after specifying the current stress state in the Gachsaran oilfield based on Anderson’s faulting theory,the reactivation tendency of four faults(F1,F2,F3,and F4)in the Asmari reservoir is analyzed using 3D Mohr diagrams and slip tendency factors.Results showed that all the faults are stable in the current stress state,and F2 has the potential to undergo the highest pore pressure build-up in the field.On the other hand,F3 has the proper conditions(i.e.,strike and dip referring to σHmax orientation)for reactivation.Stress polygons were also applied to show the effect of the pore pressure increase on fault stability,in a graphical manner.According to the results,the best location for drilling a new injection well in this part of the field is the NW side of F2,due to the lower risk of reactivation.It was found that both methods of 3D Mohr diagrams and slip tendency factors predict similar results,and with the lack of image logs for stress orientation determination,the slip tendency method can be applied.The results of such studies can also be used for locating safe injection points and determining the injection pressure prior to numerical modeling in further geomechanical studies.