A Fault Diagnosis Expert System for a Heavy Motor Used in a Rolling Mill

A fault diagnosis expert system for a heavy motor used in a rolling mill is established in this paper. The fault diagnosis knowledge base was built, and its knowledge was represented by production rules. The knowledge base includes daily inspection system, brief diagnosis system and precise diagnosis system. A pull down menu was adopted for the management of the knowledge base. The system can run under the help of expert system development tools. Practical examples show that the expert system can diagnose faults rapidly and precisely.

Application of Improved Genetic Algorithm in Network Fault Diagnosis Expert System

Knowledge acquisition is the “bottleneck” of building an expert system. Based on the optimization model, an improved genetic algorithm applied to knowledge acquisition of a network fault diagnostic expert system is proposed. The algorithm applies operators such as selection, crossover and mutation to evolve an initial population of diagnostic rules. Especially, a self adaptive method is put forward to regulate the crossover rate and mutation rate. In the end, a knowledge acquisition problem of a simple network fault diagnostic system is simulated, the results of simulation show that the improved approach can solve the problem of convergence better.

FAULT DIAGNOSIS OF ROTATING MACHINERY USING KNOWLEDGE-BASED FUZZY NEURAL NETWORK

A novel knowledge-based fuzzy neural network （KBFNN） for fault diagnosis is presented. Crude rules were extracted and the corresponding dependent factors and antecedent coverage factors were calculated firstly from the diagnostic sample based on rough sets theory. Then the number of rules was used to construct partially the structure of a fuzzy neural network and those factors were implemented as initial weights, with fuzzy output parameters being optimized by genetic algorithm. Such fuzzy neural network was called KBFNN. This KBFNN was utilized to identify typical faults of rotating machinery. Diagnostic results show that it has those merits of shorter training time and higher right diagnostic level compared to general fuzzy neural networks.

Integrated automatic HAZOP analysis and fault diagnosis based on Petri net

Based on systematically analyzing the procedure of hazard and operability (HAZOP) study, the author introduces a method of modeling fault diagnosis with the Petri net with fuzzy colors, in which the fuzzy information can be represented effectively in the process of analysis. The author proposes the architecture of a knowledge base, which integrates HAZOP analysis and fault diagnosis, and provides the conditions for constructing the knowledge-based expert system. The author also presents a method of knowledge representation for on-line HAZOP analysis and on-line fault diagnosis is presented based on the technology of Petri net with fuzzy colors, which establishes a technological fundamental for integrating the automatic HAZOP analysis and fault diagnosis.

Synthetic Intelligent Fault Diagnosis Technology for Complex Process

A fault diagnosis method of knowledge based fuzzy neural network is proposed for complex process, which is hard to develop practical mathematical model. Fault detection is performed through a knowledge based system, where fault detection heuristic rules have been generated from deep and shallow knowledge of the process. The fuzzy neural network performs the fault diagnosis task. This method does not need practical mathematical models of objects, so it is a strong implement for complex process.

Knowledge Processing Method of Fault Diagnosis Expert Systems for Letter Sorting Equipment

Based on the analysis of fault diagnosis knowledge of letter sorting machine, this paper proposes a processing method by which the fault diagnosis knowledge is divided into exact knowledge, inadequate knowledge and fuzzy knowledge. Then their presenting and implementing form in fault diagnosis expert system is discussed and studied. It is proved that the expert system has good feasibility in the field of the diagnosis of letter sorting machine.

A new interpretable fault diagnosis method based on belief rule base and probability table

It is vital to establish an interpretable fault diagnosis model for critical equipment.Belief Rule Base(BRB)is an interpretable expert system gradually applied in fault diagnosis.However,the expert knowledge cannot be utilized to establish the initial BRB accurately if there are multiple referential grades in different fault features.In addition,the interpretability of BRB-based fault diagnosis is destroyed in the optimization process,which reflects in two aspects:deviation from the initial expert judgment and over-optimization of parameters.To solve these problems,a new interpretable fault diagnosis model based on BRB and probability table,called the BRB-P,is proposed in this paper.Compared with the traditional BRB,the BRB-P constructed by the probability table is more accurate.Then,the interpretability constraints,i.e.,the credibility of expert knowledge,the penalty factor and the rule-activation factor,are inserted into the projection covariance matrix adaption evolution strategy to maintain the interpretability of BRB-P.A case study of the aerospace relay is conducted to verify the effectiveness of the proposed method.

Experts' Knowledge Fusion in Model-Based Diagnosis Based on Bayes Networks

In previous researches on a model-based diagnostic system, the components are assumed mutually independent. Howerver , the assumption is not always the case because the information about whether a component is faulty or not usually influences our knowledge about other components. Some experts may draw such a conclusion that 'if component m 1 is faulty, then component m 2 may be faulty too'. How can we use this experts' knowledge to aid the diagnosis? Based on Kohlas's probabilistic assumption-based reasoning method, we use Bayes networks to solve this problem. We calculate the posterior fault probability of the components in the observation state. The result is reasonable and reflects the effectiveness of the experts' knowledge.

STUDY OF REAL-TIME EXPERT SYSTEM TOOL FOR INDUSTRIAL FAULT MONITORING AND DIAGNOSIS

From the requirements of industrial production,an integrated fault monitoring,diagnosis and repairing system is suggested in this paper. This new scheme of fault monitoring and diagnosis system is realized by a master-slave real-time expert system,and a real-time expert system tool for this system is also developed accordingly. As an example of application of this tool ,a realtime expert system for fault monitoring and diagnosis on DC mine hoist is developed. Experiments show that this tool possesses better supporting environment, strong knowledge acquisition ability, and convenience for use. The system developed by this tool not only meets the real-time requirement of DC hoist,but also can give correct diagnosis results.

UNCERTAIN KNOWLEDGE MANAGEMENT IN EXPERT SYSTEMS USING FUZZY METAGRAPHS

This paper presented a new graph theoretic construct——fuzzy metagraphs and discussed their applications in constructing fuzzy knowledge base. Fuzzy metagraphs describe the relationships between sets of fuzzy elements but not single fuzzy element and offer some distinct advantages both for visualization of systems, as well as for formal analysis of system structure. In rule based system, a fuzzy metagraph is a unity of the knowledge base and the reasoning engine. Based on the closure of the adjacency matrix of fuzzy metagraphs, this paper presented an optimized inferential mechanism working mainly by an off line approach. It can greatly increase the efficiency of inference. Finally, it was applied in a daignostic expert system and satisfactory results were obtained.

A NEURAL NETWORK BASED FAULT FUZZY DIAGNOSTIC SYSTEM

A fault fuzzy diagnostic system(FFDS) based on neural network and fuzzy logic hybrid is proposed. FFDS consists of two modes: a fuzzy inference mode and a rule learning mode. The fuzzy inference rules are stored in the memory layer. The excitation levels of the memory neurons reflect the matching degrees between the input vectors and the prototype rules. In the rule learning mode, the rules can be produced automatically through the cluster process. As an application case of this diagnostic system, the fault diagnosis experiment of the rotating axis is simulated.

Study on Fault Diagnosis of Rotating Machinery with Hybrid Neural Networks

With the help of the feedforward neural network diagnostic method, the hybrid diagnostic networks corresponding to information in multiple symptom domains are built and the comprehensive judgment is carried out with weighted average method. Meanwhile, this method has the ability of self learning and self adaptation in order to adapt both the complexity of vibrations produced practically and the pluralistic potent of vibration symptoms induced really for large rotating machinery, especially for turbogenerators. The reliability and precision of diagnosis with this method is heightened. It seems that the method can take more practical value in engineering applications.

A DEVELOPING TOOL FOR THE EXPERT SYSTEM IN TECHNOLOGY DESIGN AND EXPERT SYSTEM FOR COCOON COOKING

This paper demonstrates the general character of some technology designing processes through their analysisand introduces a developing tool for the expert system in technology design.With this tool a particular domain ex-pert can directly establish a knowledge base so as to form a practical expert system,and thus enhanca correctness ofknowledge experssion and the developing efficiency of the expert system.It also discusses in detail the mech-anism of reasoning,interpreting and knowledge obtaining.The knowledge base consists of three parts:classifyingrules,essential data,and regulating rules.It can be formed by means of the expert dialouge and edition.In its ap-plication,the knowledge base can constantly accumulate successful experience to achieve itsself-study function.The paper shows the way to describe the knowledge in a particular domain and the process of applying this tool ina particular domain. The tool is written in Turb-Prolog language,And an expert system for cocoon cooking isprovided.

面向注塑产品工艺缺陷的知识图谱构建方法及应用

针对现有注塑产品缺陷故障原因排查与定位依靠专家人工诊断效率低、成本高昂等不足,本文提出了一种面向注塑产品缺陷的知识图谱构建方法及其应用,目的在于将专家知识采用知识图谱进行表示,利用基于知识图谱的垂直检索技术,解决故障排查和定位困难的问题.首先,文章基于多源异构的故障解决方案文本构建语料库,并构建知识本体模型.其次,采用面向非结构化文本的知识抽取模型,将产品缺陷的相关专家知识从原始语料中自动抽取出来.最后,利用Neo4j图数据库实现知识存储及可视化知识图谱的构建.在所构建的知识图谱中,探索并实现了知识智能搜索、故障诊断及工艺卡解析等应用,展示了知识图谱技术在注塑领域的良好应用前景.

城市集中供热水处理专家帮助系统的开发

随着智能城市的不断发展,专家帮助系统在人们的生活中发挥着至关重要的作用。针对热电联产机组和热水管网水处理经常出现的一些故障进行分析,将热源厂水处理中常出现的设备故障问题及其解决方案制作成数据库和专家知识库,在windows环境下,将数据库与Visual Basic6.0软件对接,开发出供水管网故障诊断专家帮助系统软件。主要功能有查询数据库中的供热管网故障类型、解决方案、即时更新数据库内容。

基于数据驱动和本体建模的数控机床主轴故障诊断与推理

针对目前数控机床主轴系统故障诊断存在方法单一及智能化程度低的问题,提出基于数据驱动和本体建模的机床主轴故障诊断与推理方法。采用EMD对传感器采集的蕴含故障特征的原始信号进行数据处理与分析,提取原始统计特征,在此基础上,构建DBN-RF诊断模型实现深度特征自适应挖掘与故障模式识别。利用Protégé5.1工具结合领域知识构建机床主轴故障本体知识库,将DBN-RF诊断模型的故障辨识结果与本体知识库中的实例进行语义映射,实现故障知识推理,获得故障原因和故障解决策略。基于采集的不同工况下轴承故障数据验证了DBN-RF诊断模型的有效性,最高故障诊断平均准确率可达92.93%;构建实例验证了本体知识库的可重用性和推理功能;最后,设计开发了数控机床主轴健康管理服务系统,实现主轴系统状态实时感知和故障诊断与推理。

基于卫星宽带的远程设备监测诊断技术研究

针对船(岛)上设备信息系统存在故障诊断不够智能化以及系统可扩展性不足等问题,提出一种利用卫星通道的远程监测诊断技术并实现其系统,该系统基于物模型规范化管理异构设备信息,船(岛)侧基于设备网关管理所有数据出入,支持利用知识库与测试集进行故障分析推理,能够很好地支撑岸端技术人员对船上设备进行智能诊断;物联网设备数据统一接入模式有效提升了系统的可扩展性。

电子电器类消费品典型故障专家知识库的构建和应用研究

本文通过对消费品产品分类和电子电器类消费品典型故障现象的分析,提出以现有的电子电器类消费品典型故障标签字典为基础建立专家知识库的思路,给出电子电器类消费品典型故障专家知识库的构建方法、体系设计、同义词扩展和故障严重度标注,以及后续专家知识库的维护和扩展。构建的电子电器类消费品典型故障专家知识库将应用于电子电器类消费品多源缺陷线索信息的标准化与归一化处理,为综合分析判定疑似缺陷案例提供科学、准确的判定依据。同时也为全品类消费品典型故障专家知识库的构建和应用奠定基础。

基于领域知识图谱的卫星故障辅助诊断与处置方法

本文重点针对传统故障诊断方法能力不足的缺点,开展基于领域知识图谱的卫星故障辅助诊断与处置方法研究,突破卫星故障领域知识图谱构建关键技术,关联固化卫星系统多维专家知识,构建卫星故障领域知识图谱,进而实现结合卫星故障知识信息的卫星故障推理分析和辅助诊断处置,提升复杂故障场景下的卫星故障诊断效率与处置能力。本文提出的方法为保障大气环境监测卫星的在轨长期稳定运行,提供了一种新的故障辅助诊断与处置措施。