Fault diagnosis of chemical processes based on partitioning PCA and variable reasoning strategy

Fault detection and identification are challenging tasks in chemical processes, the aim of which is to decide out of control samples and find fault sensors timely and effectively. This paper develops a partitioning principal component analysis(PPCA) method for process monitoring. A variable reasoning strategy is proposed and applied to recognize multiple fault variables. Compared with traditional process monitoring methods, the PPCA strategy not only reflects the local behavior of process variation in each model(each direction of principal components),but also improves the monitoring performance through the combination of local monitoring results. Then, a variable reasoning strategy is introduced to locate fault variables. Unlike the contribution plot, this method locates normal and fault variables effectively, and gives initiatory judgment for ambiguous variables. Finally, the effectiveness of the proposed process monitoring and fault variable identification schemes is verified through a numerical example and TE chemical process.

Power Transformer Fault Diagnosis Using Fuzzy Reasoning Spiking Neural P Systems

This paper presents an intelligent technique to fault diagnosis of power transformers dissolved and free gas analysis (DGA). Fuzzy Reasoning Spiking neural P systems (FRSN P systems) as a membrane computing with distributed parallel computing model is powerful and suitable graphical approach model in fuzzy diagnosis knowledge. In a sense this feature is required for establishing the power transformers faults identifications and capturing knowledge implicitly during the learning stage, using linguistic variables, membership functions with “low”, “medium”, and “high” descriptions for each gas signature, and inference rule base. Membership functions are used to translate judgments into numerical expression by fuzzy numbers. The performance method is analyzed in terms for four gas ratio (IEC 60599) signature as input data of FRSN P systems. Test case results evaluate that the proposals method for power transformer fault diagnosis can significantly improve the diagnosis accuracy power transformer.

Machinery fault diagnosis expert system based on case-based reasoning

A mechinery fault diagnosis expert system based on case-based reasoning (CBR) technology was established. The process of the CBR fault diagnosis is analyzed from three main aspects: expression and memory, retrieving and matching, and modification and maintenance of a case. The results indicate that the CBR method is flexible and simple to implement, and it has strong self-studying ability. Using a large enough number of case reasoning sets, it can accumulate the experience of problem solving, avoid the difficulty of knowledge acquisition, shorten the course of solving problems, improve efficiency of reasoning, and save the time of developing.

Application of Uncertainty Reasoning Theory to Satellite Fault Detection and Diagnosis

Reasoning theories are divided into certainty reasoning theories and uncertainty reasoning theories. Now, only certainty reasoning theories are used to detect and diagnose satellite faults. However, in practice, it is difficult to detect and diagnose some faults of the satellite automatically only by use of certainty reasoning theories. The reason is that detection and diagnosis of these faults require a rational reasoning and a fault tolerant capability. Fortunately, uncertainty reasoning theories can meet these requirements. It is attracting attention of many experts in the space field all over the world that uncertainty reasoning theories are applied to detect and diagnose satellite faults. Uncertainty reasoning theories include several kinds of theories, such as inclusion degree theory, rough set theory, evidence reasoning theory, probabilistic reasoning theory, fuzzy reasoning theory, and so on. Inclusion degree theory, rough set theory and evidence reasoning theory are three advanced ones. Based on these three theories respectively, the author introduces three new methods to detect and diagnose satellite faults in this paper. It is shown that the methods, suitable for detecting and diagnosing satellite faults, especially uncertainty faults, can remedy the defects of the current methods.

Research on Satellite Fault Diagnosis and Prediction Using Multi-modal Reasoning

Diagnosis and prediction of satellite fault are more difficult than that of other equipment due to the complex structure of satellites and the presence of multi excite sources of satellite faults. Generally, one kind of reasoning model can only diagnose and predict one kind of satellite faults. In this paper the author introduces an application of a new method using multi modal reasoning to diagnose and predict satellite faults. The method has been used in the development of knowledge based satellite fault diagnosis and recovery system (KSFDRS) successfully. It is shown that the method is effective.

装备维修保障知识模型构建及推理方法

针对装备领域维修保障知识利用效率低、经验知识转化不足、故障定位分析困难等问题,提出了一种装备维修保障知识模型构建及推理方法。首先,通过对装备维修保障工作流程、知识构成及知识特征的分析,借助知识的本体表示方法,设计装备维修保障知识模型,并结合具体装备给出知识模型构建方法;其次,基于知识模型并综合考虑人员经验性知识的量化表达,提出故障原因可信度推理方法,实现维修知识的有效推理计算;最后,以车辆装备发动机故障为例,验证了所提方法的可行性,为装备领域维修保障知识模型构建和推理应用提供借鉴。

Improved Multi-objective Ant Colony Optimization Algorithm and Its Application in Complex Reasoning

The problem of fault reasoning has aroused great concern in scientific and engineering fields.However,fault investigation and reasoning of complex system is not a simple reasoning decision-making problem.It has become a typical multi-constraint and multi-objective reticulate optimization decision-making problem under many influencing factors and constraints.So far,little research has been carried out in this field.This paper transforms the fault reasoning problem of complex system into a paths-searching problem starting from known symptoms to fault causes.Three optimization objectives are considered simultaneously: maximum probability of average fault,maximum average importance,and minimum average complexity of test.Under the constraints of both known symptoms and the causal relationship among different components,a multi-objective optimization mathematical model is set up,taking minimizing cost of fault reasoning as the target function.Since the problem is non-deterministic polynomial-hard(NP-hard),a modified multi-objective ant colony algorithm is proposed,in which a reachability matrix is set up to constrain the feasible search nodes of the ants and a new pseudo-random-proportional rule and a pheromone adjustment mechinism are constructed to balance conflicts between the optimization objectives.At last,a Pareto optimal set is acquired.Evaluation functions based on validity and tendency of reasoning paths are defined to optimize noninferior set,through which the final fault causes can be identified according to decision-making demands,thus realize fault reasoning of the multi-constraint and multi-objective complex system.Reasoning results demonstrate that the improved multi-objective ant colony optimization(IMACO) can realize reasoning and locating fault positions precisely by solving the multi-objective fault diagnosis model,which provides a new method to solve the problem of multi-constraint and multi-objective fault diagnosis and reasoning of complex system.

基于知识图谱的水电站设备故障根因分析方法

水电站设备故障成因复杂、关联性强,研究故障之间的成因关系及发生概率有助于快速确定故障原因和制定排查计划。根据专家经验与历史故障数据构建了水电站设备知识图谱,设计了基于知识图谱的智能故障诊断算法,利用Noisy Or模型实现一种近似推理算法,实现了根因的定量分析,并基于图推理分析相关现象和熵理论实现了排查建议的优化计算。该系统可给出全面、详细的建议和解释信息,允许用户自由交互,可以帮助用户快速开展排查故障。系统具有不依赖历史数据、准确性高、可解释性强、可动态更新等优点,为智慧水电站建设提供了先进平台。

一种基于贝叶斯网络的燃气轮机故障诊断方法

本文针对现役电站燃气轮机故障样本少,以往的故障诊断方法依赖于海量的带有故障标签的数据,难以在实际生产中取得预期的诊断效果的现象,提出了一种通过利用贝叶斯网络进行反事实推理来识别燃气轮机故障原因的方法。本文首先介绍了贝叶斯网络的基本原理,然后将故障模式和影响分析及故障树技术应用于贝叶斯网络的搭建,最后通过实际案例分析,验证了这一方法的有效性。本文的故障诊断方法可以根据燃气轮机在运行中出现的异常现象分析出可能的故障和相应的故障原因,帮助运行及检修人员及时发现和排除故障,并且弥补了基于数据驱动的故障诊断方法缺少专业知识支撑的缺陷,为燃气轮机的故障诊断提供了一种灵活、高效、可靠的新选择。

基于IQPSO-GA优化ANFIS模型的服务器故障预警方法

针对服务器底层部分业务类硬件故障对系统稳定运行的影响,提出一种改进的量子行为粒子群优化(IQPSO)与遗传算法(GA)相结合的混合元启发式优化算法对自适应神经模糊推理系统(ANFIS)参数进行训练,以获得更准确的ANFIS规则进行硬件故障预警的方法;首先,通过分析服务器业务与硬件相关参数之间的映射关系,通过采集的数据集对ANFIS模型进行训练构造预测模型;其次,考虑ANFIS在梯度计算过程中存在容易陷入局部最优值的问题,设计了一种IQPSO算法结合GA中的交叉和变异算子操作混合元启发算法全局搜索ANFIS规则参数;最后,通过一组后处理样本数据集对所提方法有效性和稳定性进行了检验;实验结果表明,该方法可有效预警服务器硬件故障,基于所提混合元启发优化算法获得的ANFIS模型具备更快的收敛速度和更高的全局搜索精度,与传统ANFIS模型相比泛化精度提高了47%以上。

合同法与有过失规则的目的解释——以“过错”认定为中心

合同法与有过失规则中过错的认定仰赖于对规则的目的解释。从合同法对当事人的履行利益、信赖利益和固有利益的保护来看,与有过失规则有三项规范目的:敦促当事人通力合作以助履行;指引当事人在合理信赖的基础上行事;保护当事人对相对方自我保护固有利益的信赖。在规范目的指引下,与有过失规则中的过错可以类型化为三种:未能履行履约相关的协助义务、未能在合理信赖的基础上行事、未能尽合理注意照顾自己的固有利益。在特定保护政策排斥与有过失规则的规范目的时,与有过失规则的适用应进行目的论限缩。在法律技术上体现为过错的否定,主要有两类情形,即受损方欠缺保护自身的能力、违约方相对于受损方更能避免损害的发生。在前者中,可以适当降低合理注意的标准,否定过错的成立;在后者中,考虑受损方有时依赖于相对方的知识经验,受损方与有过失成立应以相对方已尽相应的告知义务为前提。

矿井掘进机液压系统故障分析探讨

针对煤矿井下掘进机运转过程中液压系统故障频发的状况,本文通过分析各液压系统的组成及功能,对造成液压系统故障的原因进行类型划分,即划分为机械原因和人为原因。结合维修人员素质、现场条件提出了经验判断法、系统分析法和综合判断法等进行掘进机故障分析,为掘进机液压系统维修提供了多种维修思路,提高了液压设备维修效率。

交流变频器故障及维修保养探讨

在现代工业快速发展的背景下,交流变频器作为电机调速的重要装置,在工业自动化系统中起着至关重要的作用。但是随着交流变频器的广泛应用,其在运行过程中出现故障的可能性也逐渐增加。因此,对于交流变频器的故障分析、维修及保养,成为工程技术人员面临的重要课题。文章旨在深入探讨交流变频器的故障原因、维修方法及保养策略,对常见的交流变频器故障进行系统分析,探讨在故障发生时应采取的维修措施,并提出有效的保养措施,以确保交流变频器在长期运行中保持稳定可靠的性能。

基于D-S-CBR的燃料电池系统生产过程故障诊断

氢能产业发展迅速,质子交换膜燃料电池系统已实现初步应用广,在车、船、电站领域的应用逐渐推广。由于燃料电池系统的产业化开始时间有限,产品标准化程度较低,在生产过程中容易出现各种故障问题,研发、生产人员难以定位故障点及故障原因,排查及解决故障的时间长,严重影响生产进度。本研究通过将证据融合理论与案例推理故障诊断方法结合,构建一种混合数据的D-S-CBR系统,实现两种类型异构数据的融合及故障诊断。并将此研究应用于燃料电池系统的生产中,辅助研发、生产人员进行生产过程故障诊断,减少故障诊断时间,降低生产成本,提高产品质量。

基于属性可靠度置信规则库的轴承故障诊断研究

轴承故障诊断是旋转器械健康管理中的一个关键问题。然而,在工程实践中,轴承的观测数据可能会受到一些干扰因素的影响,包括传感器质量和环境中的噪声等。在传统置信规则库(BRB)中,其模型推理假定输入数据完全可靠,但不可靠的观测数据会使BRB精度降低。具有属性可靠度的置信规则库模型(BRB-r)提供了一种建模框架和分析方法,是一个能够聚合不可靠定量数据和专家知识的系统。为提高轴承故障诊断精度,提出一种基于BRBr的轴承故障诊断模型。首先,基于统计方法计算属性可靠度;然后,使用证据推理作为模型的推理机;最后,采用投影协方差矩阵自适应进化策略(P-CMA-ES)对模型进行参数优化。验证实验结果表明,BRB-r在一定程度上能够消除观测数据中不确定性信息的影响,并对不可靠数据进行有效处理,具备良好的诊断效果。

Network Fault Analysis from Passive Measurement

A new passive method for automatic discovery and location of network failure is proposed.This method employs a passive measurement to collect information and events from network traffic,and employs a model-based reasoning system to detect and locate network faults.Measurement points are deployed in a backbone network to capture the traffic and then evaluate the Quality of Service(QoS) metrics of end-to-end IP conversations.A routing model is also established for the observed network to simulate the attributes and activities of routers and links.This routing model also deduces the routing path for each IP conversation,and thus the QoS metrics of IP conversations are mapped into the metrics of paths.With the information of shared links of overlapping paths and network tomography technique,the QoS metrics of links can also be estimated,and the poorly rated links are picked out as failure points.This method is implemented in a tool named FaultMan,which is deployed in a campus network.Test results have shown its availability in middle-scale networks.

大型卫星地面站故障诊断的增强分层有向图方法

为了解决传统分层有向图模型在卫星地面站故障诊断面临的问题,提出了基于增强分层有向图模型的故障诊断方法。在建模过程中,考虑到卫星地面站故障征兆多、建模复杂,依据各设备工作状态的故障传播方式合并同类节点,以减小模型规模;同时,在模型中加入节点有效性使能函数,克服传统分层有向图模型中设备主备切换导致系统结构发生改变时需要重新建模的问题。在故障诊断推理过程中,采用反向回溯和正向推理相结合的方法减小故障源搜索空间,并基于各节点被搜索次数给出故障概率,提高诊断效率。以北斗卫星无线电测定业务地面站为例对单故障和多故障报警场景下的故障诊断方法进行了验证。结果表明,基于增强分层有向图模型的故障诊断方法可以提高故障诊断的准确率和全面性。

超深层断控缝洞型油藏油井合理产能优化方法及应用

为优化超深层断控缝洞型油藏油井合理产能,以塔里木盆地FQ油田为例,分析其地质和开发特征,利用产能试井法、嘴流法和油藏数值模拟法,对油井合理产能进行优化。结果表明,超深层断控缝洞型油藏一次采油可分为初期、中期和后期3个阶段,不同阶段开发特征差异显著。FQ油田超深层断控缝洞型油藏产能试井曲线存在下凸型、直线型、上翘型和台阶型4类,其中,下凸型曲线油井合理产能取曲线拐点处对应产能;直线型和上翘型曲线油井合理产能均取最大测试产能,但需继续扩大油嘴测试产能,直至曲线出现拐点;台阶型曲线油井合理产能取台阶后最大测试产能。嘴流法优化油井合理产能取嘴流曲线拐点对应产能。油藏数值模拟法适用于一次采油中期和后期油井合理产能优化,通过优化油井见水时间、累计产油量、采收率等关键指标,确定油井合理产能。

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.

载人密闭舱室FTA-FBN舒适度评估方法

为改善载人密闭舱室整体舒适度,提升作业人员的生理、心理舒适性与工作效率。对载人密闭舱室舒适度评估方法展开深入探究,在完成舱室整体舒适度评估的基础上,能够进一步明确影响舱室综合舒适度的基本事件并进行重要度排序,从而更有针对性的改进和指导载人密闭舱室设计。构建了载人密闭舱室舒适度故障树分析(Fault tree analysis,FTA)模型,将载人密闭舱室舒适度影响因素划分生理环境、物理因素、主观感受3个中间等级,下行提取17个基本事件,综合模糊贝叶斯(Fuzzy bayesian networks,FBN)方法进行正向诊断,评估载人密闭舱室的整体舒适性;展开逆向因果推理,寻找造成舱室不舒适的主要原因。结果表明:实例探究中选取西北工业大学载人密闭实验室进行整体舒适度评估,并通过16位被试人员的主观评价验证了载人密闭舱室FTA-FBN舒适度评估方法的有效性与可靠性;逆向推理计算各基本事件对舱室整体舒适度的影响概率并进行排序,指出载人密闭舱室设计改进方向。