应用人工神经网络专家系统进行拉深件成形缺陷分析

人工神经网络专家系统是一种新型的专家系统。阐述了人工神经网络专家系统在冲压件成形缺陷分析中的应用，着重指出了人工神经网络专家系统与传统专家系统的不同之处以及在建立冲压件缺陷分析人工神经网络专家系统时所要注意的关键技术。     

大型水蒸汽喷射泵系统故障诊断专家系统

运用水蒸汽喷射泵抽气理论，结合射泵运行实际，分析喷射泵常见故障，可能产生的原因及处理方法，形成专家系统库，用产生式规则对知识进行表达；在水蒸汽喷射泵上，安装压力，温度，流量等传感器，获得所需数据，形成专家系统数据库，当喷射泵出现故障时，采用逆向推理机制从目标出发，找出故障可能产生的原因。此外，专家系统具有自学习功能，可以提高专家系统故障诊断的针对性和准确性。     

工艺设计的专家系统—XJDCAP

本文介绍工艺设计的专家系统—XJDCAP系统,它用框架描述零件和记录工艺设计结果,用产生式规则表示车、铣、钻、磨、热处理等工艺设计中的决策知识、采用反向设计方式,进行模糊推理,可输出合理完善的工艺文件及CNC机床程序,并经验证确认是可行的。     

基于CLIPS与ANN的嵌入式故障诊断专家系统

嵌入式故障诊断技术要求针对性强、占用空间小、可配置、实时性好，传统的故障诊断技术需要大量的数据资源作为支持，不能满足以上要求。提出了一种基于C语言集成产生式系统（CLIPS）与人工神经网络技术（ANN）技术的嵌入式故障诊断专家系统E—FDES。对故障诊断专家系统及其相关技术进行了深入研究，在Linux环境下搭建了专家系统开发环境以及相应的设计工具链；基于构件化设计思想，使系统具备较好的可配置性与可扩展性，同时可以对数据处理构件以及图形用户界面构件进行裁减，以满足嵌入式系统的基本运行要求；利用CLIPS提供的应用程序接口设计实现了相对精简高效的规则库与知识库，并通过ANN信息融合算法完成判断与推理。工业应用系统实例证明：其在ARM—Linux嵌入式环境下运行稳定，上下文切换迅速，能够对一些常见故障进行有效识别。     

材料设计专家系统与人工神经网络的应用

针对材料设计的目标，介绍了两种人工智能方法，除了传统的专家系统外，着重介绍了近年来的最新进展－人工神经网络及其在设计中的应用。     

IT技术支持专家系统开发中知识表示方法的应用研究

在专家系统开发中知识表示是一项关键技术,它研究如何将领域知识和专家经验等有效地表示成计算机能够工作和运行的形式。重点介绍专家系统常用的产生式表示法和规则集知识表示方法的区别。阐述在Intranet中IT技术支持专家系统中采用规则集知识表示方法的优点。     

自适应调优专家系统和渐进的散布式优化策略

以某石化公司气体分离装置为研究对象,综合应用工艺机理分析模型、生产数据统计分析模型以及神经网络模型,总结归纳了现场装置的工艺知识和操作经验,开发了一个基于产生式规则的在线自适应调优专家系统.用渐进的散布式优化策略实施操作调整,取得了良好的效果.     

数控技术在采矿工业中的应用和展望

本文在讨论数控在采矿工业中应用现状的基础上，将着重讨论采矿系目前正在进行的研究工作，具体内容包括：专家系统、只是归纳发原理、基于实例的推理系统和人工神经网络技术。最后，作为一个智能数控程序应用实例，简要介绍了矿山锚杆支护设计与选型专家系统。     

基于产生式规则的交通违法处罚专家系统的研究与实现

根据高速公路监控指挥系统的快速、准确处罚需求,开发了基于产生式规则的违法处罚专家系统。该系统对违法处罚条例进行抽取与组织,将其形式化并初步形成产生式规则,通过专家支持进行规则的约简,存入知识库;利用合理的人机交互的界面使得用户能快速查找并定位违法事件,并遵守交通法中"一事不再罚"原则及相关处罚规定,将数据库中的信息与知识库中的产生式相匹配,输出严格的《交通违法行政处罚通知书》通过界面告知用户。另外该系统的管理模块实现了对规则对应关系的更新和修改,具有很强的灵活性。     

人工智能在金属塑性加工领域中的应用

概述了人工智能及其2个主要分支(专家系统和人工神经网络),讨论了遗传算法与专家系统集成以及神经网络与模糊逻辑、专家系统集成的必要性和集成方法,分别介绍了专家系统和人工神经网络在塑性加工领域中的应用现状.     

Application of expert systems and pattern recognition methodologies to facilities layout planning

This paper deals with two basic concepts of artificial intelligence (AI), from a facilities layout problem domain perspective. In this work, the facilities layout problem is treated as a multi-objective situation. From conventional multi-objective perspective, the philosophy underlying this work is not a different one. However, the qualitative constraints are handled via a symbolic manipulation structure. The two conceptualizations are: (a) an expert system and (b) a pattern recognition system. In the expert system, the heuristics used are based on the augmented transition networks of natural language processing. In the pattern recognition system, the use of productions rules to capture the expert knowledge is illustrated. For both the systems example problems are given.     

Use of expert systems in the design of thermal equipment and processes

A study has been carried out on the use of knowledge-based computer-aided design methodology for the design of thermal systems. An expert system is developed using a Prolog-based front end, where the design rules, material databases, computational procedures, and the relevant expert knowledge are implemented. A combination of quantitative and heuristic inputs are employed in the design process. The basic approach employs an iterative redesign strategy, starting with an initial design obtained from the available knowledge base, and the design parameters are iteratively varied until the specified design rules and constraints are satisfied. The general approach can be employed for a variety of thermal systems. The application to a practical system is demonstrated by the design of an electrical furnace used in the thermal processing of materials. The results from the numerical simulation and design of this system are presented to indicate the basic features and the versatility of the expert system.     

Use of neural networks and expert systems to control a gas/solid sorption chilling machine: Utilisation des réseaux neuronaux et des systèmes experts pour réguler une machine frigorifique à sorption gaz/solide

This works focuses on using neural networks and expert systems to control a gas/solid sorption chilling machine. In such systems, the cold production changes cyclically with time due to the batchwise operation of the gas/solid reactors. The accurate simulation of the dynamic performance of the chilling machine has proven to be difficult for standard computers when using deterministic models. Additionally, some model parameters dynamically change with the reaction advancement. A new modelling approach is presented here to simulate the performance of such systems using neural networks. The backpropagation learning rule and the sigmoid transfer function have been applied in feedforward, full connected, single hidden layer neural networks. Overall control of this system is divided in three blocks: control of the machine stages, prediction of the machine performance and fault diagnosis.     

Governing the dynamics of multi-stage production systems subject to learning and forgetting effects: A simulation study

Managing production systems where production rates change over time due to learning and forgetting effects poses a major challenge to researchers and practitioners alike. This task becomes especially difficult if learning and forgetting effects interact across different stages in multi-stage production systems as rigid production management rules are unable to capture the dynamic character of constantly changing production rates. In a comprehensive simulation study, this paper first investigates to which extent typical key performance indicators (KPIs), such as the number of setups, in-process inventory, or cycle time, are affected by learning and forgetting effects in serial multi-stage production systems. The paper then analyses which parameters of such production systems are the main drivers of these KPIs when learning and forgetting occur. Lastly, it evaluates how flexible production control based on Goldratt’s Optimised Production Technology can maximise the benefits learning offers in such systems. The results of the paper indicate that learning and forgetting only have a minor influence on the number of setups in serial multi-stage production systems. The influence of learning and forgetting on in-process inventory and cycle time, in contrast, is significant, but ambiguous in case of in-process inventory. The proposed buffer management rules are shown to effectively counteract this ambiguity.     

Expert systems in production and operations management: research directions in assessing overall impact

The use and development of expert systems in public and private organizations continue to increase. Many of these systems are being developed for production and operations management. Unfortunately, the impacts that these systems are having in these environments have, for the most part, not been investigated. Most studies on expert systems to date centre either on the technical aspects or validation issues. No one has taken a systemic view that takes into account both the technical issues and the human issues that will have to be addressed in implementing these systems. This paper seeks to stimulate research into the overall impact of expert systems implementation in production. To this end, fourteen research propositions are developed and presented. In addition, the major variables associated with these propositions are combined into a causal model to show the relationships between them and to reveal an overall perspective of the impacts of expert systems implementation on the production process.     

Development of a knowledge-based decision support system for diagnosing malfunctions of advanced production equipment

This paper presents a case study documenting the development of an expert system for diagnosing the malfunctions of a machine used by the NEC Corporation to mount chips on integrated circuit boards. Development of the expert system was justified by the inability of operators to efficiently diagnose many malfunctions of the chip-mounting machine, the associated cost of production delays, and the disruption incurred when experts were forced to leave unrelated tasks to help operators troubleshoot malfunctions. The first step in development of the expert system was to elicit and organize the machine designer's knowledge. This process resulted in a hierarchical classification of malfunction symptoms and causes, a set of 15 flow diagrams documenting the designer's troubleshooting procedures for particular malfunction symptoms, and a matrix documenting design information. The flow diagrams were translated into a large logic network diagram, which was directly translated into a set of 94 rules. An additional set of 270 rules were derived from the design matrix. The resulting 364 rules were then implemented in an expert system using the KES shell. On-site validation revealed that 92% of the chip-mounting machine's malfunctions occurring in 1988-1989 were successfully diagnosed by the expert system. Future directions of this research will be oriented toward the development of a general purpose expert system capable of diagnosing the malfunctions of other similar production equipment.     

A comparison of sequencing rules in static and dynamic hybrid flow systems

It is well known that efficient scheduling of jobs is essential for improving the economics of production in manufacturing organizations. As a result, extensive research has been conducted on scheduling, especially in job shop and flow shop settings. In contrast, little research has been done on hybrid flow systems, even though they are found in many industries, including beer processing, glass container production, pertroleum refining, plastic-coated cable production, and fertilizer production. Furthermore, the few studies that have dealt with hybrid systems have been limited by the assumptions made about their operating environments. Therefore, we conducted a study that extends the previous work on hybrid systems in two significant ways: (1) it included financially oriented scheduling rules and a new, related performance measure; and (2) the new rules were compared with the existing ones in a large simulation experiment under both static and dynamic (generally encountered in practice) hybrid flow shop environments. To date such comparisons have been made only under static environments. The results show that the relative performances of the scheduling rules differ as the assumptions regarding the operating environment are changed.     

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 diagno-sis 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.     

TREE-EXPERT: a tree-based expert system for fault tree construction

This paper addresses the topic of automatic fault tree construction, utilizing an expert system with Artificial Intelligence (AI) techniques and presents the related software tool, TREE-EXPERT—an expert system for automatic fault tree construction. In the light of the features involved in developing a fault tree, a new and more reasonable structure of knowledge representation, which is knowledge tree based, has been established. The knowledge tree provides the means by which component failure behaviors can be described by a group of particular fault tree modules instead of production rules. By introducing the conditional branch function, the new design of the knowledge base incorporates many good features such as strong expressivity, flexibility and ease of extension and it takes advantage of the user's familiarity with the field of fault tree analysis. Additionally, the design of the inference engine is original in that it deals with nodes, which it treats, as special components, so that many complicated engineering cases, such as the application of success criteria, and the problems of flow diversions and flow reversals in a process system, can be well managed and the function of the expert system is improved as a whole. TREE-EXPERT can be used to deal with large-scale and complicated engineering systems, and many engineering factors can be considered, e.g. more than one system parameter and the effect on them switching of the system operating states, bi-directional inference, human error failure, common-cause failure, maintenance and test, etc. On the other hand, the software uses P & ID (Pipe & Instrument Diagram) type interface to describe the system topology, which provides an easier man-machine interface with powerful graphics functions. This software can handle not only ‘process’ systems but also, with appropriate additions to the generic knowledge base, electrical systems and other similar systems.     

An application of an expert system in layout compaction of VLSI design

Abstract

Recent research in knowledge‐based expert systems of VLSI design tools has concentrated on placement, routing, and cell generation. This paper presents an alternative application for artificial intelligence (AI) techniques on compaction design for a VLSI mask layout‐expert compactor. In order to overcome the shortcomings of iterative search through a large problem space within a working memory, and therefore, to speed‐up the runtime of compaction, a set of rule‐based region query operations and knowledge‐based techniques for the plane sweep method are proposed in this system. Experimental results have explored the possibility of using expert system technology (EST) to automate the compaction process by “reasoning” out the layout design and applying sophisticated expert rules to its knowledge base.