Fault detection and identification based on combining logic and model in a wall-climbing robot

A combined logic- and model-based approach to fault detection and identification （FDI） in a suction foot control system of a wall-climbing robot is presented in this paper. For the control system, some fault models are derived by kinematics analysis. Moreover, the logic relations of the system states are known in advance. First, a fault tree is used to analyze the system by evaluating the basic events （elementary causes）, which can lead to a root event （a particular fault）. Then, a multiple-model adaptive estimation algorithm is used to detect and identify the model-known faults. Finally, based on the system states of the robot and the results of the estimation, the model-unknown faults are also identified using logical reasoning. Experiments show that the proposed approach based on the combination of logical reasoning and model estimating is efficient in the FDI of the robot.

FAULT DIAGNOSIS EXPERT SYSTEM FOR ROTATING MACHINERY BASED ON A FUZZY PROBABILITY LOGIC INFERENCE MODEL

A new theory- the fuzzy probability logic theory is presented , This theory incorpo- rates the genterally-used fuzzy logic and the traditionally-used probability logic theory in attempt to emulate the rational fault diagnosis under uncertainty. According to the theory , an inference model , named as FSL , is thus designed to be devoted to the building of a fault diagnosis expert system for rotating machinery (ROSLES) . The system is put into operation on a vibration simula- tor stand for 300 MW turbine generator set ( 1 : 1 0) and satisfactory results are gained.

Power transformer fault diagnosis model based on rough set theory with fuzzy representation

Objective Due to the incompleteness and complexity of fault diagnosis for power transformers,a comprehensive rough-fuzzy scheme for solving fault diagnosis problems is presented.Fuzzy set theory is used both for representation of incipient faults' indications and producing a fuzzy granulation of the feature space.Rough set theory is used to obtain dependency rules that model indicative regions in the granulated feature space.The fuzzy membership functions corresponding to the indicative regions,modelled by rules,are stored as cases.Results Diagnostic conclusions are made using a similarity measure based on these membership functions.Each case involves only a reduced number of relevant features making this scheme suitable for fault diagnosis.Conclusion Superiority of this method in terms of classification accuracy and case generation is demonstrated.

Intelligent Fault Diagnosis in Lead-zinc Smelting Process

According to the fault characteristic of the imperial smelting process （ISP）, a novel intelligent integrated fault diagnostic system is developed. In the system fuzzy neural networks are utilized to extract fault symptom and expert system is employed for effective fault diagnosis of the process. Furthermore, fuzzy abductive inference is introduced to diagnose multiple faults. Feasibility of the proposed system is demonstrated through a pilot plant case study.

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few com- prehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition moni- toring and fault diagnosis. The recent research and devel- opment of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are dis- cussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mech- anism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suit- able method for a specific situation and pointing out potential research directions.

Fault Diagnosis Technology Based on Model Driven

Fault diagnosis is an important application of the power grids monitoring system. Under the situation of continuous development of smart grid, it brings new challenges to the fault diagnosis technology. A fault diagnosis expert system based on model driven approach is proposed in this paper. And the corresponding fault modeling technology based on Fault Logic Description Language (FLDL) is described step by step. Practices show that this system could meet the requirements of processing fault alarm information rapidly and reliably by operator.

Intelligent Process Fault Diagnosis for Nonlinear Systems with Uncertain Plant Model via Extended State Observer and Soft Computing

There have been many studies on observer-based fault detection and isolation (FDI), such as using unknown input observer and generalized observer. Most of them require a nominal mathematical model of the system. Unlike sensor faults, actuator faults and process faults greatly affect the system dynamics. This paper presents a new process fault diagnosis technique without exact knowledge of the plant model via Extended State Observer (ESO) and soft computing. The ESO’s augmented or extended state is used to compute the system dynamics in real time, thereby provides foundation for real-time process fault detection. Based on the input and output data, the ESO identifies the un-modeled or incorrectly modeled dynamics combined with unknown external disturbances in real time and provides vital information for detecting faults with only partial information of the plant, which cannot be easily accomplished with any existing methods. Another advantage of the ESO is its simplicity in tuning only a single parameter. Without the knowledge of the exact plant model, fuzzy inference was developed to isolate faults. A strongly coupled three-tank nonlinear dynamic system was chosen as a case study. In a typical dynamic system, a process fault such as pipe blockage is likely incipient, which requires degree of fault identification at all time. Neural networks were trained to identify faults and also instantly determine degree of fault. The simulation results indicate that the proposed FDI technique effectively detected and isolated faults and also accurately determine the degree of fault. Soft computing (i.e. fuzzy logic and neural networks) makes fault diagnosis intelligent and fast because it provides intuitive logic to the system and real-time input-output mapping.

Fault current Characterization Based on Fuzzy Algorithm for DOCR Application

Penetration of distribution generation (DG) into power system might disturb the existing fault diagnosis system. The detection of fault, fault classification, and random changes of direction of fault current cannot always be monitored and determined via on-line by conventional fault diagnosis system due to DG penetration. In this paper, a fault current characterization which based on fuzzy logic algorithm (FLA) is proposed. Fault detection, fault classification, and fault current direction are extracted after processing the measurement result of three-phase line current. The ability of fault current characterization based on FLA is reflected in directional overcurrent relay (DOCR) model. The proposed DOCR model has been validated in microgrid test system simulation in Matlab environment. The simulation result showed accurate result for different fault location and type. The proposed DOCR model can operate as common protection device (PD) unit as well as unit to improve the effectiveness of existing fault diagnosis system when DG is present.

Current Mode Logic Testing of XOR/XNOR Circuit: A Case Study

This paper investigates the issue of testing Current Mode Logic (CML) gates. A three-bit parity checker is used as a case study. It is first shown that, as expected, the stuck-at fault model is not appropriate for testing CML gates. It is then proved that switching the order in which inputs are applied to a gate will affect the minimum test set;this is not the case in conventional voltage mode gates. Both the circuit output and its inverse have to be monitored to reduce the size of the test set.

基于相关性分离的逻辑电路敏感门定位算法

随着CMOS器件特征尺寸进入纳米量级,因高能粒子辐射等造成的电路失效问题日益严重,给电路可靠性带来严峻挑战。现阶段,准确评估集成电路可靠性,并以此为依据对电路进行容错加固,以提高电路系统可靠性变得刻不容缓。然而,由于逻辑电路中存在大量扇出重汇聚结构,由此引发的信号相关性导致可靠性评估与敏感单元定位面临困难。该文提出一种基于相关性分离的逻辑电路敏感门定位算法。先将电路划分为多个独立电路结构(ICS);以ICS为基本单元分析故障传播及信号相关性影响;再利用相关性分离后的电路模块和反向搜索算法精准定位逻辑电路敏感门单元;最后综合考虑面向输入向量空间的敏感门定位及针对性容错加固。实验结果表明,所提算法能准确、高效地定位逻辑电路敏感单元,适用于大规模及超大规模电路的可靠性评估与高效容错设计。

25 Hz相敏轨道电路与ZPW-2000A结合处的逻辑检查故障分析

25 Hz相敏轨道电路与ZPW-2000A轨道电路特性不同,但在自动闭塞及站内电码化改造过程中两种制式轨道电路常结合应用。不同的特性会导致结合处的逻辑检查失效。针对场联进路无法正常解锁、三接近闭塞分区遗留失去分路故障进行分析,介绍故障发生的场景及原因,并讨论计算机联锁、列控中心、区间综合监控系统以及继电逻辑检查电路对于此类故障的解决方案,为后续工程设计及故障处理提供参考。

不确定性智能车辆转向系统的容错控制方法研究

对存在执行器死区、故障现象和模型不确定性的智能车辆转向系统的跟踪控制问题进行研究。首先,采用自适应模糊逻辑系统对转向系统中难以建模的非线性项进行在线逼近,并结合动态增益技术为智能车辆转向系统设计了自适应滑模控制器,以补偿系统控制增益未知、执行器死区和故障现象对控制性能的影响。此外,在该滑模控制器的设计中,采用一阶滤波技术消除了滑模控制器对转向系统造成的抖振。最后,结合李雅普诺夫稳定性理论分析可知,所设计的控制器可以实现智能车辆转向系统的渐近稳定,并通过数值仿真和整车实验验证了所设计的控制方法的合理性。

考虑移动储能车提升系统供电可靠性的配电网规划方法

传统配电网规划通常是先规划再进行可靠性的校核,难以将可靠性校核结果反馈以有效修正网架规划方案,因此需要建立考虑配电网可靠性的规划方法。而随着配电网发展,移动和固定式储能得到广泛应用。通过多类型储能的优化配置,可在故障发生时维持关键负荷的供电,提高城市配电网可靠性。首先,采用了一种基于故障时序逻辑的可靠性指标解析表达方法,将可靠性指标约束嵌入传统配电网规划模型。然后,在运行约束中考虑移动储能车以及固定式储能参与恢复供电的故障时序逻辑,建立改进的配电网规划模型。该模型实现了计及移动和固定储能对可靠性影响的配电网规划,可采用混合整数线性规划求解器求解。最后,通过24节点实际配电网算例证明了所提方法的有效性。

本特利3500振动监测系统在管线压缩机组的故障分析

介绍了本特利3500振动监测系统在川气东送管线离心压缩机组振动监测过程中的应用,阐明了其系统结构、硬件选型、逻辑功能及故障处理方法。

基于FBPNN的火电厂锅炉故障诊断研究

在神经网络(NN)基础上,将模糊逻辑引入反向传播(BP)神经网络中,构建了模糊BP神经网络(FBPNN),并将其应用于火电厂锅炉故障诊断中。FBPNN在锅炉故障样本上进行了训练和测试,误差分析表明,与传统的标准BP网络相比,FBPNN能获得更好的锅炉故障诊断结果。

基于逻辑运算的配电网故障定位方法及算例分析

为提高配电网故障定位准确率,针对传统算法模型在故障区段定位方面的不足,提出一种基于逻辑运算的故障定位方法。利用逻辑运算来描述节点和断面之间的因果关系,并构建故障定位的数值计算模型。仿真实验结果表明,粒子群优化算法和深度学习算法的准确率未达到100%,且粒子群优化算法的召回率最小为84%,而逻辑运算方法对不同预设故障区域的单一故障的定位准确率和定位召回率均为100%。因此,文章提出的基于逻辑运算的配电网故障定位方法准确率最高,具有较好的应用价值。

基于PLC的自动测流机器人系统设计

针对当前河道测流存在的效率和自动化程度较低、测量模式单一等问题,提出了一套基于可编程逻辑控制器(PLC)的河道自动测流机器人系统设计与实现方案。该机器人系统由运动系统、采集系统、人机交互以及远程通信四大模块组成。首先,设计了自动测流机器人系统的硬件结构,实现了自动测流机器人在河流轨道上运动、铅鱼收放等功能。然后,开发了基于PLC的测控系统,设计了PLC梯形图和硬件连接方案,实现了自动测流机器人运动控制。最后,自动测流机器人系统通过RS-485通信协议将多传感器采集的数据传输到操控屏,结合4G远程技术,实现了采集数据和操作指令的远程传输。通过现场模拟的采集测试,该机器人系统可实现自动测流、远程操控、故障自检等功能,并提供多种测量模式,满足河道测流的使用需求。该系统的设计为开展智能化河流数据采集的研究奠定了基础。

一种体现构网型控制特征的柔性直流机电暂态改进建模方法

总结柔性直流采用构网型控制呈现的新特性并提出相应的机电暂态建模方法。通过分析,提出构网型控制替代跟网型控制给柔性直流带来的主要变化是:电压源支撑,故障穿越切换措施以及电流限幅方法。相对应的,构网控制型柔性直流机电暂态模型是对跟网控制型柔性直流模型的改进,包括将换流器与交流网络的接口由传统的一个交流仿真步长内的平均功率改为平均电压;增加故障穿越措施切换逻辑以及增加基于用户自定义的电流限幅措施。通过交直流间接口方法仿真对比,以及机电、电磁暂态模型故障模拟验证所提模型的有效性。该模型能高精度模拟构网控制型柔性直流在交流电网中的动态行为,具有良好的应用价值。

CRH1A平台动车组主控手柄故障分析

动车组主控手柄是司机用来操纵动车组运行的主令控制器,也是司机用来发出动车组运行过程中牵引或制动指令的主令控制设备,其功能的稳定性直接影响动车组运用安全。以CRH1A平台动车组实际运用过程中发生主控手柄故障为线索,从主控手柄控制功能及诊断机理,深入分析主控手柄故障模式及故障原因,发现主控手柄源头设计缺陷及运用检修标准不合理的问题,提出通过优化诊断逻辑、完善检修标准及进一步研究主控手柄国产化替代原有进口主控手柄等防控措施,提高了主控手柄控制稳定性,保障了动车组运行安全。

基于信号对比的电路板故障诊断技术

操作便捷、诊断准确、执行高效一直是测井仪器电路板维修的方向。本文从大部分测井仪器电路板的电气特性入手,基于可将多种工作电源、程控波形发生电路和处理采集电路整合在一起的电路板检测装置,阐述了建立标准电路板各测量点的标准波形库的方法和被检测板的检测流程,通过信号对比异常的逻辑关系,建立故障点智能诊断结论,实现了专家诊断功能,通过应用案列阐述了该技术应用效果及前景。