RH-KTB炉外精炼过程的故障诊断专家系统

主要介绍了本专家系统的知识表达、获取及推理机等，并以诊断报告的形式将诊断的结果解释给使用者。     

基于模糊理论的黑体炉系统故障诊断的设计及实现

为了实现黑体炉系统已发故障或潜在故障的快速高效诊断,根据黑体炉系统运行特性,建立了多库多层次方式的知识库,并采用黑体炉故障类型诊断的反向推理和故障,原因分析及故障消除措施正向推理相结合的混合推理机制,设计了总体目标推理和级目标推理相结合的推理机。应用效果表明,黑体炉故障诊断专家系统的预报准确率较高,具有较大的实用性。     

液压系统故障诊断专家系统的建立

1　综述随着现代化进程的加快 ,建立自动化、智能化的液压故障诊断专家系统已是迫在眉睫。进行液压系统故障诊断的根本目的在于保证液压系统能够可靠而有效地发挥其功能 ,主要包含三点 :首先是保证液压系统无故障 ,运行可靠 ;其次是“物尽其能”,保证液压系统发挥其最大效益 ;然后是要保证液压系统如将有故障或已发生故障 ,能及时而正确地诊断 ,以减少维修时间 ,提高维修质量 ,保证正常运行。诊断的第一步是提取液压系统状态的特征信号。一般说来液压系统的特征信号具有两种表现形式 ,一种是可以以能量方式表现的特征信号 :压力、振动、噪声…     

水轮发电机组故障诊断专家系统知识库的设计

介绍了专家系统的基本原理,知识获取和表示的基本概念,讨论了现在了为广泛的规则表示法,结合水轮发电机组故障诊断实例,对专家知识库的设计策略,实施步骤和组织结构进行了详细论述,并就目前应用于故障诊断领域的神经网络技术进行了探讨。     

船用柴油机故障诊断专家系统知识库的构建

以开发某船用柴油机故障诊断系统为目标,根据柴油机常见故障的形式及其特征,按照专家系统设计的一般原则,利用C＋＋Builder开发工具,建立了专家系统及其知识库的总体结构,包括事实库、规则库、初始数据库以及维修记录库等。利用SQLServer作为数据库平台,建立数据袁以及各表之间的联系,实现了专家系统的各种功能。并用实例...     

面向多知识库的故障诊断推理机的设计

在系统研究二叉树、语义树和α－β搜索算法的基础上,提出了知识库组织结构设计方法和搜索策略。并且,为了实现多知识库的集成,有效综合专家的经验知识,利用α－β搜索算法和框结构知识表示方法设计了一个具有可视化功能的实时故障诊断推理机。     

旋转机械故障诊断专家系统的设计和实现

根据故障诊断反问题的研究成果,利用ＶｉｓｕａｌＣ＋＋５．０编程环境,设计了旋转机械故障诊断专家系统。论述了系统的设计思想以及系统实现的理论和技术,介绍了系统的功能,特点和应用情况。     

火电厂真空系统的在线监测与故障诊断专家系统

为保证火电厂真空系统的安全经济优化运行，实现故障诊断和操作指导，开发了该真空系统在线监测与故障诊断专家系统。介绍了该系统的基本结构、主要功能和应用实例。系统中采用了多种故障诊断相结合的方法，如基于规则的专家系统和基于神经网络的故障诊断方法。     

旋转机械振动故障诊断知识库的研究与实现

重点收集整理了曲型旋转机械振动故障的故障特征，以这些故障特征为基础，应用专家系统的基本原理和诊断知识处理的方法，建立起旋转机械振动故障诊断知识库，借助专家系统工具CLIPS编写知识库，实现了基本的旋转机械振动故障诊断。     

电喷发动机故障诊断专家系统

本文介绍了控制燃油喷射发动机故障诊断专家系统的基本结构和功能。此系统通过对采集到的冷却水温度、起动电流、起动电压、氧传感器信号、进气歧管压力、爆燃传感器信号及转速等参数和进行了数据分析，并且据此诊断电喷发动机的故障。还阐述了故障诊断专家系统的有效性和实用性。     

锅炉故障诊断专家系统的知识表达

针对循环流化床锅炉实际应用对象的特性，采用面向对象的方法对其进行了分析与描述，提出了现阶段可以实际应用于锅炉系统的故障诊断专家系统的知识处理方案，详细叙述了框架、规则、过程的知识表达方法。     

机车电器电路故障诊断系统的研究

针对机车电气线路故障诊断难度较大的状况,提出了机车电器电路故障诊断系统的基本组成,并对车载诊断系统的基本结构,硬件结构,软件设计进行了介绍。     

A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks

This work proposes a novel fault diagnostic technique for photovoltaic systems based on Artificial Neural Networks (ANN). For a given set of working conditions - solar irradiance and photovoltaic (PV) module's temperature - a number of attributes such as current, voltage, and number of peaks in the current–voltage (I–V) characteristics of the PV strings are calculated using a simulation model. The simulated attributes are then compared with the ones obtained from the field measurements, leading to the identification of possible faulty operating conditions. Two different algorithms are then developed in order to isolate and identify eight different types of faults. The method has been validated using an experimental database of climatic and electrical parameters from a PV string installed at the Renewable Energy Laboratory (REL) of the University of Jijel (Algeria). The obtained results show that the proposed technique can accurately detect and classify the different faults occurring in a PV array. This work also shows the implementation of the developed method into a Field Programmable Gate Array (FPGA) using a Xilinx System Generator (XSG) and an Integrated Software Environment (ISE).     

Sensors fault diagnosis and fault tolerant control for grid connected PV system

This paper discusses a Sensors Fault Detection and Isolation (FDI) and Fault Tolerant Control (FTC) of three phase inverter for PV system application. All sensors used in the inverter control strategy are addressed in this work. The developed FDI algorithm focuses on residuals generation; it is defined by the different between measurement and reconstituted signal. So, estimators block are investigated, such as, two Models References Adaptive Systems (MRAS) are developed to reconstitute the information of three phase line currents and dc link voltage sensors. The reconstituted information is injected to the corresponding FDI algorithm. Grid voltage sensors FDI is presented in this contribution trough the use of scaled virtual flux estimator, based on Second Order Generalized Integrator and Frequency Looked Loop. When a fault detected, FTC is ensured by replacing the erroneous measurement by its corresponding estimator. Theoretical study and simulations results are presented to validate the effectiveness of the proposed method.     

基于模型的航天器推进系统故障诊断

设计了基于模型的推进系统的故障诊断系统,根据推进系统的结构和行为模型,分析出各组件可处的工作状态及组件之间的连接关系,利用JMPL建模语言建立了推进系统各组件的定性模型。把推进系统模型和系统场景文件输入给诊断推理引擎,可实现推进系统的实时诊断。诊断结果表明,建立的推进系统模型是准确可靠的,开发的诊断系统能有效地找出故障组件并确定故障组件的状态。     

基于机车故障诊断专家系统的故障树优化设计

针对基于故障树的机车故障诊断专家系统,通过引入二叉故障树的概念,将传统故障树合成为一棵二叉故障树模型,并以此为基础,对其知识库的存储和故障诊断方式进行了研究,达到了优化系统的目的。     

Robust fault diagnosis and fault tolerant control for PEMFC system based on an augmented LPV observer

In order to improve the safety and reliability of proton exchange membrane fuel cell system, this paper proposes a novel robust fault observer for the fault diagnosis and reconstruction of the PEMFC air management system. First, considering the complexity and large computation of the nonlinear PEMFC system, a linear parameter-varying (LPV) model is introduced to describe the system behavior and reduce the computation cost. Then, an augmented state observer based on the LPV model is proposed for simultaneously estimating the internal states and component faults. The robustness is guaranteed by taking the system disturbances and measurement noises into consideration when designing the observer gain. The observer design is transformed into a process of solving a set of linear inequality matrices. According to the results, the augmented robust observer can accurately estimate the system states and faults under different conditions. Moreover, to realize the fault tolerant control of the air supply, the oxygen stoichiometry estimator is designed taking consideration of system fault information and a corresponding controller is employed for air compressor voltage following the net power maximization strategy.     

Data-driven fault diagnosis for PEMFC systems of hybrid tram based on deep learning

The running state of the hybrid tram and the service life of fuel cell stacks are related to the fault diagnosis strategy of the proton exchange membrane fuel cell (PEMFC) system. In order to accurately detect various fault types, a novel method is proposed to classify the different health states, which is composed of simulated annealing genetic algorithm fuzzy c-means clustering (SAGAFCM) and deep belief network (DBN) combined with synthetic minority over-sampling technique (SMOTE). Operation data generated by the tram are clustered by SAGAFCM algorithm, and valid data are selected as fault diagnosis samples which include the training sample and the test sample. However, the fault samples are usually unbalanced data. To reduce the influence of unbalanced data on the fault diagnosis accuracy, SMOTE is employed to form a new training sample by supplementing the data of the small sample. Then DBN is trained by the new training sample to obtain the fault diagnosis model. In this paper, the proposed method can well distinguish the four health states, which are high deionized water inlet temperature fault, hydrogen leakage fault, low air pressure fault and the normal state, with an accuracy of 99.97% for the training sample and 100% for the test sample.     

火电厂锅炉报警诊断专家系统的设计与实现

为了及时、方便地根据火电厂锅炉系统的报警信息诊断出故障原因,并为设备提供检修参考和决策意见,使用Visual C 和专家系统工具CLIPS开发了火电厂锅炉报警诊断专家系统.简单介绍了使用VC 与CLIPS结合开发的原因.给出了该系统的总体结构,介绍了其基本功能.用Visual C 实现了专家系统的界面、报警信息的输入、数据的处理等外壳部分.CLIPS用来实现诊断的核心部分包括知识库的构建和推理机的实现.使用动态链接库DLL嵌入式实现了Visual C 和CLIPS的混合编程.最后给出了系统运行实例.开发的专家系统功能能够根据用户提供的报警信息及时、在线地给出故障原因和操作指导.系统界面友好,功能完善,知识库易于维护和更新.