Fault diagnosis of electronic systems using intelligent techniques: a review
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paper_title: System Test And Diagnosis

paper_content:

Part One: Motivation. 1. Introduction. 2. Maintainability: a Historical Perspective. 3. Field Diagnosis and Repair: the Problem. Part Two: Analysis and Application. 4. Bottom-Up Modeling for Diagnosis. 5. System Level Analysis for Diagnosis. 6. The Information Flow Model. 7. System Level Diagnosis. 8. Evaluating System Diagnosability. 9. Verification and Validation. 10. Architecture for System Diagnosis. Part Three: Advanced Topics. 11. Inexact Diagnosis. 12. Partitioning Large Problems. 13. Modeling Temporal Information. 14. Adaptive Diagnosis. 15. Diagnosis -- Art versus Science. References. Index.
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paper_title: Knowledge-based systems for instrumentation diagnosis, system configuration and circuit and system design

paper_content:

Abstract A number of knowledge-based systems in the electronic engineering field have been developed in the past decade. These include those that use knowledge-based techniques to diagnose instrumentation, determine system configurations and to aid circuit and system design. This paper reviews the literature on knowledge-based systems for electronic engineering applications in these areas and reports progress made in the development of a basis for realising electronic systems.
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paper_title: Pimtool, an expert system to troubleshoot computer hardware failures

paper_content:

This paper describes a tool to diagnose the cause of failure of a HP computer server. We do this by analyzing the dump of Processor Internal Memory (PIM) and maximizing the leverage of expert learning from one hardware failure situation to another. The tool is a rule-based expert system, with some nested rules which translate to decision trees. The rules were implemented using a metalanguage which was customized for the hardware failure analysis problem domain. Pimtool has been deployed to 25 users as of December 1996. We plan to expand usage to over 400 users by end of 1997. Using Pimtool, we expect to save over 15 minutes in Mean-Time-to-Repair (MTIR) per call. We have recognized that knowledge management will be a key issue in the future and are developing tools and strategies to address it.
---
paper_title: Artificial Intelligence: Structures and Strategies for Complex Problem Solving

paper_content:

Artificial Intelligence: Structures and Strategies for Complex Problem Solving by George F. Luger 6th edition, Addison Wesley, 2008 The book serves as a good introductory textbook for artificial intelligence, particularly for undergraduate level. It covers major AI topics and makes good connection between different areas of artificial intelligence. Along with each technique and algorithm introduced in the book, is a discussion of its complexity and application domain. There is an attached website to the book that provides auxiliary materials for some chapters, sample problems with solutions, and ideas for student projects. Besides Prolog and Lisp, java and C++ are also used to implement many of the algorithms in the book. The book is organized in five parts. The first part (chapter 1) gives an overview of AI, its history and its various application areas. The second part (chapters 2–6) concerns with knowledge representation and search algorithms. Chapter 2 introduces predicate calculus as a mathematical tool for representing AI problems. The state space search as well as un-informed and heuristic search methods is introduced in chapters 3 and 4. Chapter 5 discusses the issue of uncertainty in problem solving and covers the foundation of stochastic methodology and its application. In chapter 6 the implementation of search algorithms is shown in production system and blackboard architectures. Part 3 (chapters 7–9) discusses knowledge representation and different methods of problem solving, including strong, weak and distributed problem solving. Chapter 7 begins with reviewing the history of evolution of AI representation schemes, including semantic networks, frames, scripts and conceptual graphs. This chapter ends with a brief introduction of Agent problem solving. Chapter 8 presents the production model and rule-based expert systems as well as case-based and model-based reasoning. The methods of dealing with various aspects of uncertainty are discussed in chapter 9. These methods include Dempster-Shafer theory of evidence, Bayesian and Belief networks, fuzzy logics and Markov models. Part 4 is devoted to machine learning. Chapter 10 describes algorithms for symbol-based learning, including induction, concept learning, vision-space search and ID3. The neural network methods for learning, such as back propagation, competitive, Associative memories and Hebbian Coincidence learning were presented in chapter 11. Genetic algorithms and evolutionary learning approaches are introduced in chapter 12. Chapter 13 introduces stochastic and dynamic models of learning along with Hidden Markov Models, Dynamic Baysian networks and Markov Decision Processes. Part 5 (chapters 14 and 15) examines two main application of AI: automated reasoning and natural language understanding. Chapter 14 begins with an introduction to weak methods in problem solving and continues with presenting resolution theorem proving. Chapter 15 deals with the complex issue of natural language understanding by discussing main methods of syntax and semantic analysis of natural language corpus. The chapter ends with examples of natural language application in Database query generation, text summarization and question answering systems. Finally, chapter 16 is a summary of the materials covered in the book as well current AI's limitations and future directions. One criticism about the book would be that the materials are not covered in enough depth. Because of the space limitation, many important AI algorithms and techniques are discussed briefly without providing enough details. As a result, some chapters (e.g., 8, 9, 11, and 13) of the book should be supported by complementary materials to make it understandable for undergraduate students and motivating for graduate students. Another issue is with the structure of the book. The order of presenting chapters introduces sequentially different challenges and techniques in problem solving. Consequently, some topics such as uncertainty and logic are not introduced separately and are distributed in different chapters of the book related to different parts. Although interesting, this makes the book hard to follow. In summary, the book gives a great insight to the readers that want to familiar themselves with artificial intelligence. It covers a broad range of topics in AI problem solving and its practical application and is a good reference for an undergraduate level introductory AI class. Elham S. Khorasani, Department of Computer Science Southern Illinois University Carbondale, IL 62901, USA
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paper_title: Implementing an expert system for fault diagnosis of electronic equipment

paper_content:

Abstract The aim of this work is to develop a rule-based expert system to aid an operator in the fault diagnosis of the electronics of forge press equipment. It is a menu-driven package, developed in Turbo PROLOG on an IBM PC., to help the operator fix faults up to replaceable module level. The system has been broadly categorised into eight sub-systems, and the rules, based on fault cause relations, have been developed for each of the sub-systems. This modular development reduces the access time, and also facilitates the handling of the knowledge base.
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paper_title: ESPCRM—an expert system for personal computer repair and maintenance

paper_content:

Abstract This paper describes the design and implementation of an expert system for personal computer repair and maintenance (ESPCRM). Based on the Personal Consultant Plus 4.0 expert system shell, ESPCRM provides consultation for the repair and maintenance of the whole series of IBM/IBM compatible PCs from the XT to 486-based machines. Troubleshooting a personal computer (PC) is a knowledge-intensive task. Depending on the experience of the technician, a simple problem could take hours or even days to solve. An expert system offers a viable solution to the problem. Presently, the knowledge base of the expert system developed consists of some 94 rules, 68 parameters and 40 graphic pages. The acquisition of knowledge is conducted through interviews with technicians in the PC repair workshop catering for some 1200 PCs of various makes, models and configurations within the Nanyang Technological University (NTU).
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paper_title: Model-based diagnosis of analog electronic circuits

paper_content:

Diagnosing analog systems, i.e. systems for which physical quantities vary over time in a continuous range is, in itself, a difficult problem. Analog electronic circuits, especially those with feedback loops, raise new difficulties that cannot be solved by using classical techniques. This paper shows how model-based diagnosis theory can be used to diagnose analog circuits. The two main tasks for making the theory applicable to real size problems will be emphasized: the modeling of the system to be diagnosed, and the building of efficient conflict recognition engines adapted to the formalism used for the modeling. This will be illustrated through the description of two systems. The first one, DEDALE, only considers failures observable in quiescent mode. It uses qualitative modeling based on relative orders of magnitude relations, for which an axiomatics is given, thus allowing a symbolic solver for checking consistency of such relations to be developed. The second one, CATS/DIANA, deals with time variations. It uses modeling based on numeric intervals, arrays of such intervals to represent transient signals, and an ATMS-like domain-independent conflict recognition engine, CATS. This engine is able to work on such data and to achieve interval propagation through constraints in such a way as to focus on the detection of all minimal nogoods. It is thus well adapted for diagnosing continuous time-varying physical systems. Experimental results of the two systems are given through various types of circuits.
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paper_title: Diagnosing circuits with state: an inherently underconstrained problem

paper_content:

“Hard problems” can be hard because they are computationally intractable. or because they are underconstrained. Here we describe candidate generation for digital devrces with state, a fault localization problem that is intractable when the devices are described at low levels of abstraction, and is underconstrained when described at higher levels of abstraction. Previous v;ork [l] has shown that a fault in a combinatorial digital circuit can be localized using a constraint-based representation of structure and behavior. ln this paper we (1) extend this represerltation to model a circuit with state by choosrng a time granularity and vocabulary of signals appropriate to that circuit; (2) demonstrate that the same candidate generation procedure that works for combinatorial circuits becomes indiscriminate when applied to a state circuit modeled in that extended representationL(3) show how the common technique of singlestepping can be viewed as a divide-and-conquer approach to overcoming that lack of constraint; and (4) illustrate how using structural de?ail can help to make the candidate generator discriminating once again, but only at great cost.
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paper_title: Diagnostic reasoning based on structure and behavior

paper_content:

Abstract We describe a system that reasons from first principles, i.e., using knowledge of structure and behavior. The system has been implemented and tested on several examples in the domain of troubleshooting digital electronic circuits. We give an example of the system in operation, illustrating that this approach provides several advantages, including a significant degree of device independence, the ability to constrain the hypotheses it considers at the outset, yet deal with a progressively wider range of problems, and the ability to deal with situations that are novel in the sense that their outward manifestations may not have been encountered previously. As background we review our basic approach to describing structure and behavior, then explore some of the technologies used previously in troubleshooting. Difficulties encountered there lead us to a number of new contributions, four of which make up the central focus of this paper. • — We describe a technique we call constraint suspension that provides a powerful tool for troubleshooting. • — We point out the importance of making explicit the assumptions underlying reasoning and describe a technique that helps enumerate assumptions methodically. • — The result is an overall strategy for troubleshooting based on the progressive relaxation of underlying assumptions. The system can focus its efforts initially, yet will methodically expand its focus to include a broad range of faults. • — Finally, abstracting from our examples, we find that the concept of adjacency proves to be useful in understanding why some faults are especially difficult to diagnose and why multiple representations are useful.
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paper_title: Qualitative Reasoning: A Survey of Techniques Applications

paper_content:

After preliminary work in economics and control theory, qualitative reasoning emerged in AI at the end of the 70s and beginning of the 80s, in the form of Naive Physics and Commonsense Reasoning. This way was progressively abandoned in aid of more formalised approaches to tackle modelling problems in engineering tasks. Qualitative Reasoning became a proper subfield of AI in 1984, the year when several seminal papers developed the foundations and the main concepts that remain topical today. Since then Qualitative Reasoning has considerably broadened the scope of problems addressed, investigating new tasks and new systems, such as natural systems. This paper gives a survey of the development of Qualitative Reasoning from the 80s, focusing on the present state-of-the-art of the mathematical formalisms and modelling techniques, and presents the principal domains of application through the applied research done in France.
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paper_title: Generation of diagnostic trees by means of simplified process models and machine learning

paper_content:

Abstract Fault diagnosis by means of diagnostic trees is of considerable interest for industrial applications. The drawbacks of this approach are mostly related to the knowledge elicitation through laborious enumeration of the tree structure and ad hoc threshold selection for symptoms definition. These problems can be alleviated if a more profound knowledge of the process is brought into play. The main idea of the paper consists of modeling the nominal and faulty states of the plant by means of interval-like component models derived from first-principles laws, e.g. the conservation law. Such a model serves to simulate the entire system under different fault conditions, in order to obtain the representative patterns of measurable process quantities, i.e. training examples. To match these patterns by diagnostic rules, multistrategy machine learning is applied. As a result, binary decision trees that relate symptoms to faults are obtained, along with the thresholds defining the symptoms. This technique is applied to a laboratory test process operating in the steady state, and is shown to be suitable for handling incipient single faults. The proposed learning approach is compared with two related machine learning methods. It is found that it achieves similar classification accuracy with better transparency of the resulting diagnostic system.
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paper_title: Troubleshooting: When Modeling Is the Trouble

paper_content:

This paper shows how order of magnitude reasoning has been successfully used for troubleshooting complex analog circuits. The originality of this approach was to be able to remove the gap between the information required to apply a general theory of diagnosis and the limited information actually available. The expert's ability to detect a defect by reasoning about the significant changes in behavior it induces is extensively exploited here: as a kind of reasoning that justifies the qualitative modeling, as a heuristic that defmes a strategy and as a working hypothesis that makes clear the scope of this approach.
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paper_title: AUTOMATIC FAULT-TREE GENERATION

paper_content:

Abstract This paper presents an expert system approach to off-line generation and optimisation of fault-trees for use in on-line fault diagnosis systems, incorporating the knowledge and experience of manufacturers and users. The size of the problem is such that explicit formulation of the fault-tree is very complicated. The knowledge, however, is implicitly available in the description of faults in terms of symptom-codes, results of performed tests and repair actions. Case-based reasoning is selected for the implementation to facilitate the automatic generation, consistency checking and maintenance of the fault-tree. Different diagnosis systems for different levels of diagnosis tasks can be generated automatically from the same problem description. Special attention is given to the processing speed needed for on-line use on modern transportation systems.
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paper_title: Encapsulation and diagnosis with fault dictionaries

paper_content:

To date, test and diagnosis has been domain knowledge driven. However, as system complexity grows and we strive to develop reusable components, the concept of encapsulation becomes increasingly important. Encapsulation embodies the concepts of separation and partitioning. In this paper we deal with encapsulation by illustration of the fault dictionary approach to digital electronics. We then extend the concept of encapsulation to the system test approach as well as the development of maintenance systems. Finally we develop the concept that encapsulation is a key element in achieving general standardization open system architectures.
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paper_title: A Probabilistic Causal Model for Diagnostic Problem Solving Part I: Integrating Symbolic Causal Inference with Numeric Probabilistic Inference

paper_content:

The issue of how to effectively integrate and use symbolic causal knowledge with numeric estimates of probabilities in abductive diagnostic expert systems is examined. In particular, a formal probabilistic causal model that integrates Bayesian classification with a domain-independent artificial intelligence model of diagnostic problem solving (parsimonious covering theory) is developed. Through a careful analysis, it is shown that the causal relationships in a general diagnostic domain can be used to remove the barriers to applying Bayesian classification effectively (large number of probabilities required as part of the knowledge base, certain unrealistic independence assumptions, the explosion of diagnostic hypotheses that occurs when multiple disorders can occur simultaneously, etc.). Further, this analysis provides insight into which notions of "parsimony" may be relevant in a given application area. In a companion paper, Part Two, a computationally efficient diagnostic strategy based on the probabilistic causal model discussed in this paper is developed.
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paper_title: A Probabilistic Causal Model for Diagnostic Problem Solving Part II: Diagnostic Strategy

paper_content:

An important issue in diagnostic problem solving is how to generate and rank plausible hypotheses for a given set of manifestations. Since the space of possible hypotheses can be astronomically large if multiple disorders can be present simultaneously, some means is required to focus an expert system's attention on those hypotheses most likely to be valid. A domain-independent algorithm is presented that uses symbolic causal knowledge and numeric probabilistic knowledge to generate and evaluate plausible hypotheses during diagnostic problem solving. Given a set of manifestations known to be present, the algorithm uses a merit function for partially completed competing hypotheses to guide itself to the provably most probable hypothesis or hypotheses.
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paper_title: Model-Based Diagnosis under Real-World Constraints

paper_content:

I report on my experience over the past few years in introducing automated, model-based diagnostic technologies into industrial settings. In partic-ular, I discuss the competition that this technology has been receiving from handcrafted, rule-based diagnostic systems that has set some high standards that must be met by model-based systems before they can be viewed as viable alternatives. The battle between model-based and rule-based approaches to diagnosis has been over in the academic literature for many years, but the situation is different in industry where rule-based systems are dominant and appear to be attractive given the considerations of efficiency, embeddability, and cost effectiveness. My goal in this article is to provide a perspective on this competition and discuss a diagnostic tool, called DTOOL/CNETS, that I have been developing over the years as I tried to address the major challenges posed by rule-based systems. In particular, I discuss three major features of the developed tool that were either adopted, designed, or innovated to address these challenges: (1) its compositional modeling approach, (2) its structure-based computational approach, and (3) its ability to synthesize embeddable diagnostic systems for a variety of software and hardware platforms.
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paper_title: Application of a bayesian network to integrated circuit tester diagnosis

paper_content:

Research efforts to implement a Bayesian belief-network-based expert system to solve a real-world diagnostic problem-the diagnosis of integrated circuit (IC) testing machines-are described. The development of several models of the IC tester diagnostic problem in belief networks also is described, the implementation of one of these models using symbolic probabilistic inference (SPI) is outlined, and the difficulties and advantages encountered are discussed. It was observed that modeling with interdependencies in belief networks simplifies the knowledge engineering task for the IC tester diagnosis problem, by avoiding procedural knowledge and focusing on the diagnostic component's interdependencies. Several general model frameworks evolved through knowledge engineering to capture diagnostic expertise that facilitated expanding and modifying the networks. However, model implementation was restricted to a small portion of the modeling, that of contact resistance failures, which were due to time limitations and inefficiencies in the prototype inference software we used. Further research is recommended to refine existing methods, in order to speed evaluation of the models created in this research. With this accomplished, a more complete diagnosis can be achieved
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paper_title: AN INTELLIGENT APPROACH TO AUTOMATIC TEST EQUIPMENT

paper_content:

In diagnosing a failed system, a smart technician would choose tests to be performed based on the context of the situation. Currently, test program sets do not fault-. isolate within the context of a situation. Instead, testing follows a rigid, predetermined, fault-isolation sequence that is based on an embedded fault tree. Current test programs do not tolerate instrument failure and cannot redirect testing by incorporating new information. However, there is a new approach to automatic testing that emulates the best features of a trained technician yet, unlike the development of rule-based expert systems, does not require a trained technician to build the knowledge base. This new approach is model-based and has evolved over the last 10 years. This evolution has led to the development of several maintenance tools and an architecture for intelligent automatic test equipment (ATE). The architecture has been implemented for testing two cards from an AV-8B power supply.
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paper_title: System Test And Diagnosis

paper_content:

Part One: Motivation. 1. Introduction. 2. Maintainability: a Historical Perspective. 3. Field Diagnosis and Repair: the Problem. Part Two: Analysis and Application. 4. Bottom-Up Modeling for Diagnosis. 5. System Level Analysis for Diagnosis. 6. The Information Flow Model. 7. System Level Diagnosis. 8. Evaluating System Diagnosability. 9. Verification and Validation. 10. Architecture for System Diagnosis. Part Three: Advanced Topics. 11. Inexact Diagnosis. 12. Partitioning Large Problems. 13. Modeling Temporal Information. 14. Adaptive Diagnosis. 15. Diagnosis -- Art versus Science. References. Index.
---
paper_title: Artificial Intelligence: Structures and Strategies for Complex Problem Solving

paper_content:

Artificial Intelligence: Structures and Strategies for Complex Problem Solving by George F. Luger 6th edition, Addison Wesley, 2008 The book serves as a good introductory textbook for artificial intelligence, particularly for undergraduate level. It covers major AI topics and makes good connection between different areas of artificial intelligence. Along with each technique and algorithm introduced in the book, is a discussion of its complexity and application domain. There is an attached website to the book that provides auxiliary materials for some chapters, sample problems with solutions, and ideas for student projects. Besides Prolog and Lisp, java and C++ are also used to implement many of the algorithms in the book. The book is organized in five parts. The first part (chapter 1) gives an overview of AI, its history and its various application areas. The second part (chapters 2–6) concerns with knowledge representation and search algorithms. Chapter 2 introduces predicate calculus as a mathematical tool for representing AI problems. The state space search as well as un-informed and heuristic search methods is introduced in chapters 3 and 4. Chapter 5 discusses the issue of uncertainty in problem solving and covers the foundation of stochastic methodology and its application. In chapter 6 the implementation of search algorithms is shown in production system and blackboard architectures. Part 3 (chapters 7–9) discusses knowledge representation and different methods of problem solving, including strong, weak and distributed problem solving. Chapter 7 begins with reviewing the history of evolution of AI representation schemes, including semantic networks, frames, scripts and conceptual graphs. This chapter ends with a brief introduction of Agent problem solving. Chapter 8 presents the production model and rule-based expert systems as well as case-based and model-based reasoning. The methods of dealing with various aspects of uncertainty are discussed in chapter 9. These methods include Dempster-Shafer theory of evidence, Bayesian and Belief networks, fuzzy logics and Markov models. Part 4 is devoted to machine learning. Chapter 10 describes algorithms for symbol-based learning, including induction, concept learning, vision-space search and ID3. The neural network methods for learning, such as back propagation, competitive, Associative memories and Hebbian Coincidence learning were presented in chapter 11. Genetic algorithms and evolutionary learning approaches are introduced in chapter 12. Chapter 13 introduces stochastic and dynamic models of learning along with Hidden Markov Models, Dynamic Baysian networks and Markov Decision Processes. Part 5 (chapters 14 and 15) examines two main application of AI: automated reasoning and natural language understanding. Chapter 14 begins with an introduction to weak methods in problem solving and continues with presenting resolution theorem proving. Chapter 15 deals with the complex issue of natural language understanding by discussing main methods of syntax and semantic analysis of natural language corpus. The chapter ends with examples of natural language application in Database query generation, text summarization and question answering systems. Finally, chapter 16 is a summary of the materials covered in the book as well current AI's limitations and future directions. One criticism about the book would be that the materials are not covered in enough depth. Because of the space limitation, many important AI algorithms and techniques are discussed briefly without providing enough details. As a result, some chapters (e.g., 8, 9, 11, and 13) of the book should be supported by complementary materials to make it understandable for undergraduate students and motivating for graduate students. Another issue is with the structure of the book. The order of presenting chapters introduces sequentially different challenges and techniques in problem solving. Consequently, some topics such as uncertainty and logic are not introduced separately and are distributed in different chapters of the book related to different parts. Although interesting, this makes the book hard to follow. In summary, the book gives a great insight to the readers that want to familiar themselves with artificial intelligence. It covers a broad range of topics in AI problem solving and its practical application and is a good reference for an undergraduate level introductory AI class. Elham S. Khorasani, Department of Computer Science Southern Illinois University Carbondale, IL 62901, USA
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paper_title: An incremental retrieval mechanism for case-based electronic fault diagnosis

paper_content:

One problem with using CBR for diagnosis is that a full case description may not be available at the beginning of the diagnosis. The standard CBR methodology requires a detailed case description in order to perform case retrieval and this is often not practical in diagnosis. We describe two fault diagnosis tasks where many features may make up a case description but only a few features are required in an individual diagnosis. We evaluate an incremental CBR mechanism that can initiate case retrieval with a skeletal case description and will elicit extra discriminating information during the diagnostic process.
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paper_title: A CBR application: service productivity improvement by sharing experience

paper_content:

Field service is now recognized as one of the most important corporate activities in order to improve customer satisfaction and to compete successfully world-wide competition. Sharing repair experience with a state-of-the-art computer technology is a key issue to improve the productivity of field service. We have developed a diagnostic expert system, named Doctor, which employs case-based reasoning (CBR) and lists the most necessary ten service parts from a product type and some symptoms acquired from a service-request call. In this paper, we describe the Doctor system and explain how accurate and reliable product-type case-bases are generated and updated from the troubleshooting experience and the generic case base, i.e., general diagnostic knowledge. We also demonstrate the effectiveness of our system with experimental results using real repair cases. >
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paper_title: Circuit diagnosis support system for electronics assembly operations

paper_content:

Abstract Diagnosis and repair operations are often major bottlenecks in electronics circuit assembly operations. Increasing board density and circuit complexity have made fault diagnosis difficult. But, with shrinking product life cycles and increasing competition, quick diagnosis and feedback is critical for cost control, process improvement, and timely product introduction. This paper describes a case-based diagnosis support system to improve the effectiveness and efficiency of circuit diagnosis in electronics assembly facilities. The system stores individual diagnostic instances rather than general rules and algorithmic procedures, and prioritizes the tests during the sequential testing process. Its knowledge base grows as new faults are detected and diagnosed by the analyzers. The system provides distributed access to multiple users, and incorporates on-line updating features that make it quick to adapt to changing circumstances. Because it is easy to install and update, this method is well-suited for real manufacturing applications. We have implemented a prototype version, and tested the approach in an actual electronics assembly environment. We describe the system's underlying principles, discuss methods to improve diagnostic effectiveness through principled test selection and sequencing, and discuss managerial implications for successful implementation.
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paper_title: Explanation-based learning with diagnostic models

paper_content:

The author discusses an approach to identifying and correcting errors in diagnostic models using explanation based learning. The approach uses a model of the system to be diagnosed that may have missing information about the relationships between tests and possible diagnoses. In particular, he uses a structural model or information flow model to guide diagnosis. When misdiagnosis occurs, the model is used to determine how to search for the actual fault through additional testing. When the fault is identified, an explanation is constructed from the original misdiagnosis and the model is modified to compensate for the incorrect behavior of the system. >
---
paper_title: Artificial Intelligence: Structures and Strategies for Complex Problem Solving

paper_content:

Artificial Intelligence: Structures and Strategies for Complex Problem Solving by George F. Luger 6th edition, Addison Wesley, 2008 The book serves as a good introductory textbook for artificial intelligence, particularly for undergraduate level. It covers major AI topics and makes good connection between different areas of artificial intelligence. Along with each technique and algorithm introduced in the book, is a discussion of its complexity and application domain. There is an attached website to the book that provides auxiliary materials for some chapters, sample problems with solutions, and ideas for student projects. Besides Prolog and Lisp, java and C++ are also used to implement many of the algorithms in the book. The book is organized in five parts. The first part (chapter 1) gives an overview of AI, its history and its various application areas. The second part (chapters 2–6) concerns with knowledge representation and search algorithms. Chapter 2 introduces predicate calculus as a mathematical tool for representing AI problems. The state space search as well as un-informed and heuristic search methods is introduced in chapters 3 and 4. Chapter 5 discusses the issue of uncertainty in problem solving and covers the foundation of stochastic methodology and its application. In chapter 6 the implementation of search algorithms is shown in production system and blackboard architectures. Part 3 (chapters 7–9) discusses knowledge representation and different methods of problem solving, including strong, weak and distributed problem solving. Chapter 7 begins with reviewing the history of evolution of AI representation schemes, including semantic networks, frames, scripts and conceptual graphs. This chapter ends with a brief introduction of Agent problem solving. Chapter 8 presents the production model and rule-based expert systems as well as case-based and model-based reasoning. The methods of dealing with various aspects of uncertainty are discussed in chapter 9. These methods include Dempster-Shafer theory of evidence, Bayesian and Belief networks, fuzzy logics and Markov models. Part 4 is devoted to machine learning. Chapter 10 describes algorithms for symbol-based learning, including induction, concept learning, vision-space search and ID3. The neural network methods for learning, such as back propagation, competitive, Associative memories and Hebbian Coincidence learning were presented in chapter 11. Genetic algorithms and evolutionary learning approaches are introduced in chapter 12. Chapter 13 introduces stochastic and dynamic models of learning along with Hidden Markov Models, Dynamic Baysian networks and Markov Decision Processes. Part 5 (chapters 14 and 15) examines two main application of AI: automated reasoning and natural language understanding. Chapter 14 begins with an introduction to weak methods in problem solving and continues with presenting resolution theorem proving. Chapter 15 deals with the complex issue of natural language understanding by discussing main methods of syntax and semantic analysis of natural language corpus. The chapter ends with examples of natural language application in Database query generation, text summarization and question answering systems. Finally, chapter 16 is a summary of the materials covered in the book as well current AI's limitations and future directions. One criticism about the book would be that the materials are not covered in enough depth. Because of the space limitation, many important AI algorithms and techniques are discussed briefly without providing enough details. As a result, some chapters (e.g., 8, 9, 11, and 13) of the book should be supported by complementary materials to make it understandable for undergraduate students and motivating for graduate students. Another issue is with the structure of the book. The order of presenting chapters introduces sequentially different challenges and techniques in problem solving. Consequently, some topics such as uncertainty and logic are not introduced separately and are distributed in different chapters of the book related to different parts. Although interesting, this makes the book hard to follow. In summary, the book gives a great insight to the readers that want to familiar themselves with artificial intelligence. It covers a broad range of topics in AI problem solving and its practical application and is a good reference for an undergraduate level introductory AI class. Elham S. Khorasani, Department of Computer Science Southern Illinois University Carbondale, IL 62901, USA
---
paper_title: Artificial Intelligence: Structures and Strategies for Complex Problem Solving

paper_content:

Artificial Intelligence: Structures and Strategies for Complex Problem Solving by George F. Luger 6th edition, Addison Wesley, 2008 The book serves as a good introductory textbook for artificial intelligence, particularly for undergraduate level. It covers major AI topics and makes good connection between different areas of artificial intelligence. Along with each technique and algorithm introduced in the book, is a discussion of its complexity and application domain. There is an attached website to the book that provides auxiliary materials for some chapters, sample problems with solutions, and ideas for student projects. Besides Prolog and Lisp, java and C++ are also used to implement many of the algorithms in the book. The book is organized in five parts. The first part (chapter 1) gives an overview of AI, its history and its various application areas. The second part (chapters 2–6) concerns with knowledge representation and search algorithms. Chapter 2 introduces predicate calculus as a mathematical tool for representing AI problems. The state space search as well as un-informed and heuristic search methods is introduced in chapters 3 and 4. Chapter 5 discusses the issue of uncertainty in problem solving and covers the foundation of stochastic methodology and its application. In chapter 6 the implementation of search algorithms is shown in production system and blackboard architectures. Part 3 (chapters 7–9) discusses knowledge representation and different methods of problem solving, including strong, weak and distributed problem solving. Chapter 7 begins with reviewing the history of evolution of AI representation schemes, including semantic networks, frames, scripts and conceptual graphs. This chapter ends with a brief introduction of Agent problem solving. Chapter 8 presents the production model and rule-based expert systems as well as case-based and model-based reasoning. The methods of dealing with various aspects of uncertainty are discussed in chapter 9. These methods include Dempster-Shafer theory of evidence, Bayesian and Belief networks, fuzzy logics and Markov models. Part 4 is devoted to machine learning. Chapter 10 describes algorithms for symbol-based learning, including induction, concept learning, vision-space search and ID3. The neural network methods for learning, such as back propagation, competitive, Associative memories and Hebbian Coincidence learning were presented in chapter 11. Genetic algorithms and evolutionary learning approaches are introduced in chapter 12. Chapter 13 introduces stochastic and dynamic models of learning along with Hidden Markov Models, Dynamic Baysian networks and Markov Decision Processes. Part 5 (chapters 14 and 15) examines two main application of AI: automated reasoning and natural language understanding. Chapter 14 begins with an introduction to weak methods in problem solving and continues with presenting resolution theorem proving. Chapter 15 deals with the complex issue of natural language understanding by discussing main methods of syntax and semantic analysis of natural language corpus. The chapter ends with examples of natural language application in Database query generation, text summarization and question answering systems. Finally, chapter 16 is a summary of the materials covered in the book as well current AI's limitations and future directions. One criticism about the book would be that the materials are not covered in enough depth. Because of the space limitation, many important AI algorithms and techniques are discussed briefly without providing enough details. As a result, some chapters (e.g., 8, 9, 11, and 13) of the book should be supported by complementary materials to make it understandable for undergraduate students and motivating for graduate students. Another issue is with the structure of the book. The order of presenting chapters introduces sequentially different challenges and techniques in problem solving. Consequently, some topics such as uncertainty and logic are not introduced separately and are distributed in different chapters of the book related to different parts. Although interesting, this makes the book hard to follow. In summary, the book gives a great insight to the readers that want to familiar themselves with artificial intelligence. It covers a broad range of topics in AI problem solving and its practical application and is a good reference for an undergraduate level introductory AI class. Elham S. Khorasani, Department of Computer Science Southern Illinois University Carbondale, IL 62901, USA
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paper_title: New developments using AI in fault diagnosis

paper_content:

Abstract This paper is intended to give a survey on the state of the art of model-based fault diagnosis for dynamic processes employing artificial intelligence approaches. Emphasis is placed upon the use of fuzzy models for residual generation and fuzzy logic for residual evaluation. By the suggestion of a knowledge-based observer-like concept for residual generation, the basic idea of a novel observer concept, the so-called “knowledge observer”, is introduced. The neural-network approach for residual generation and evaluation is outlined as well.
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paper_title: Fault diagnosis in systems using fuzzy logic

paper_content:

Two tasks of fault detection in linear dynamical systems are addressed in this paper. On one hand, to estimate residuals, a system described by a model with some deviations in parameters or unknown input disturbances is considered. In such a situation, sensor fault detection using classical methods is not very efficient. In order to solve this problem, an adaptive thresholding approach using fuzzy logic is proposed. On the other hand, to locate faults, a fuzzy logic technique is put in place of usual classical logic used with dedicated observer scheme. >
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paper_title: Test and diagnosis of analog circuits: When fuzziness can lead to accuracy

paper_content:

Testing and diagnosing analog circuits is a very challenging problem. The inaccuracy of measurement and the infinite domain of possible values are the principal difficulties. AI approaches were the base of many systems which tried to overcome these problems. The first part of this paper is a state of the art of this research area. We present two fundamental approaches, model-based reasoning and qualitative reasoning, and the systems implementing them; a discussion and an evaluation of these systems are given. In the second part, we present and propose a novel approach based on fuzzy logic in order to go further in dealing with analog circuits testing and diagnosis. Tolerance is treated by means of fuzzy intervals which are more general, more efficient and of higher fidelity to represent the imprecision in its different forms than other approaches. Fuzzy intervals are also able to be semi-qualitative which is more suitable to the simulation of analog systems. We use this idea to develop a best test point finding strategy based on fuzzy probabilities and fuzzy decision-making methodology. Finally, a complete expert system which implements this approach is presented.
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paper_title: FLAMES: A Fuzzy Logic ATMS and Model-based Expert System for Analog Diagnosis

paper_content:

Diagnosing analog circuits with their numerous known difficulties is a very hard problem. Digital approaches have proven to be inappropriate, and AI-based ones suffer from many problems. In this paper we present a new system, FLAMES, which uses fuzzy logic, model-based reasoning, ATMS extension, and the human expertise in an appropriate combination to go far in the treatment of this problem.
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paper_title: On fuzzy logic applications for automatic control, supervision, and fault diagnosis

paper_content:

The degree of vagueness of variables, process description, and automation functions is considered and is shown. Where quantitative and qualitative knowledge is available for design and information processing within automation systems. Fuzzy-rule-based systems with several levels of rules form the basis for different automation functions. Fuzzy control can be used in many ways, for normal and for special operating conditions. Experience with the design of fuzzy controllers in the basic level is summarized, as well as criteria for efficient applications. Different fuzzy control schemes are considered, including cascade, feedforward, variable structure, self-tuning, adaptive and quality control leading to hybrid classical/fuzzy control systems. It is then shown how fuzzy logic approaches can be applied to process supervision and to fault diagnosis with approximate reasoning on observed symptoms. Based on the properties of fuzzy logic approaches the contribution gives a review and classification of the potentials of fuzzy logic in process automation.
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paper_title: Artificial neural network based multiple fault diagnosis in digital circuits

paper_content:

The paper describes a technique, based on the use of Artificial Neural Networks (ANNs), for the diagnosis of multiple faults in digital circuits. The technique utilises different quantities of randomly selected circuit test data derived from a fault truth table, which is constructed by inserting random single stuck-at faults in the circuit. The paper describes the diagnostic procedure using the technique, the ANN architecture and results obtained with example circuits. Our results demonstrate that when the test data selection procedure is guided by test vectors of the circuit a compact, efficient and flexible ANN architecture is achieved.
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paper_title: New developments using AI in fault diagnosis

paper_content:

Abstract This paper is intended to give a survey on the state of the art of model-based fault diagnosis for dynamic processes employing artificial intelligence approaches. Emphasis is placed upon the use of fuzzy models for residual generation and fuzzy logic for residual evaluation. By the suggestion of a knowledge-based observer-like concept for residual generation, the basic idea of a novel observer concept, the so-called “knowledge observer”, is introduced. The neural-network approach for residual generation and evaluation is outlined as well.
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paper_title: Neural network recognition of electronic malfunctions

paper_content:

Neural network software can be applied to manufacturing process control as a tool for diagnosing the state of an electronic circuit board. The neural network approach significantly reduces the amount of time required to build a diagnostic system. This time reduction occurs because the ordinary combinatorial explosion in rules for identifying faulted components can be avoided. Neural networks circumvent the combinatorial explosion by taking advantage of the fact that the fault characteristics of multiple simultaneous faults frequently correlate to the fault characteristics of the individual faulted components. This article clearly demonstrates that state-of-the-art neural networks can be used in automatic test equipment for iterative diagnosis of electronic circuit board malfunctions.
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paper_title: Experience in using neural networks for electronic diagnosis

paper_content:

British Telecommunication plc (BT) has an interest in developing fast, efficient diagnostic systems especially for high volume circuit boards as found in today's digital telephone exchanges. Previous work to produce a diagnostic system for line cards has shown that a model-based, expert system shell can be most beneficial in assisting in the diagnosis and subsequent repair of these complex, mixed-signal cards. Expert systems, however successful, can take a long time to develop in terms of knowledge acquisition, model building and rule development. The re-emergence of neural networks stimulated the authors to develop a system that would diagnose common faults found on line cards by training a network using historical test data.
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paper_title: System Test And Diagnosis

paper_content:

Part One: Motivation. 1. Introduction. 2. Maintainability: a Historical Perspective. 3. Field Diagnosis and Repair: the Problem. Part Two: Analysis and Application. 4. Bottom-Up Modeling for Diagnosis. 5. System Level Analysis for Diagnosis. 6. The Information Flow Model. 7. System Level Diagnosis. 8. Evaluating System Diagnosability. 9. Verification and Validation. 10. Architecture for System Diagnosis. Part Three: Advanced Topics. 11. Inexact Diagnosis. 12. Partitioning Large Problems. 13. Modeling Temporal Information. 14. Adaptive Diagnosis. 15. Diagnosis -- Art versus Science. References. Index.
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paper_title: Diagnosis of multifaults in analogue circuits using multilayer perceptrons

paper_content:

It is shown, by means of an example, how multiple faults in bipolar analogue integrated circuits can be diagnosed, and their resistances determined, from the magnitudes of the Fourier harmonics in the spectrum of the circuit responses to a sinusoidal input test signal using a two-stage multilayer perceptron (MLP) artificial neural network arrangement to classify the responses to the corresponding fault. A sensitivity analysis is performed to identify those harmonic amplitudes which are most sensitive to the faults, and also to which faults the functioning of the circuit under test is most sensitive. The experimental and simulation procedures are described. The procedures adopted for data preprocessing and for training the MLPs are given. One hundred percent diagnostic accuracy was achieved, and most resistances were determined with tolerable accuracy.
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paper_title: Fault diagnosis in complex systems using artificial neural networks

paper_content:

Very complex technical and other physical processes require sophisticated methods of fault diagnosis and online condition monitoring. Various conventional techniques have already been well investigated and presented in the literature. However, in the last few years, a lot of attention has been given to adaptive methods based on artificial neural networks, which can significantly improve the symptom interpretation and system performance in a case of malfunctioning. Such methods are especially considered in cases where no explicit algorithms or models for the problem under investigation exist. In such problems, automatic interpretation of faulty symptoms with the use of artificial neural network classifiers is recommended. Two different models of artificial neural networks, the extended backpropagation and the radial basis function, are discussed and applied with appropriate simulations for a real world applications in a chemical manufacturing plant. >
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paper_title: Test and diagnosis of analog circuits: When fuzziness can lead to accuracy

paper_content:

Testing and diagnosing analog circuits is a very challenging problem. The inaccuracy of measurement and the infinite domain of possible values are the principal difficulties. AI approaches were the base of many systems which tried to overcome these problems. The first part of this paper is a state of the art of this research area. We present two fundamental approaches, model-based reasoning and qualitative reasoning, and the systems implementing them; a discussion and an evaluation of these systems are given. In the second part, we present and propose a novel approach based on fuzzy logic in order to go further in dealing with analog circuits testing and diagnosis. Tolerance is treated by means of fuzzy intervals which are more general, more efficient and of higher fidelity to represent the imprecision in its different forms than other approaches. Fuzzy intervals are also able to be semi-qualitative which is more suitable to the simulation of analog systems. We use this idea to develop a best test point finding strategy based on fuzzy probabilities and fuzzy decision-making methodology. Finally, a complete expert system which implements this approach is presented.
---
paper_title: Research Perspectives and Case Studies in System Test and Diagnosis

paper_content:

Preface. 1. Diagnostic Inaccuracies: Approaches to Mitigate W.R. Simpson. 2.Pass/Fail Limits - The Key to Effective Diagnostic Tests H. Dill. 3. Fault Hypothesis Computations Using Fuzzy Logic T.M. Bearse, M.L. Lynch. 4. Deriving a Diagnostic Inference Model from a Test Strategy T.M. Bearse. 5. Inducing Diagnostic Inference Models from Case Data J.W. Sheppard. 6. Accurate Diagnosis Through Conflict Management J.W. Sheppard, W.R. Simpson. 7. System Level Test Process Characterization and Improvement D. Farren, et al. 8. A Standard for Test and Diagnosis J. Taylor. 9. Advanced Onboard Diagnostic System for Vehicle Management K. Keller, et al. 12. Combining Model-Based and Case-Based Expert Systems M. Ben-Bassat, et al. 11. Enhanced Sequential Diagnosis A. Biasizzo, et al. Subject Index.
---
paper_title: FLAMES: A Fuzzy Logic ATMS and Model-based Expert System for Analog Diagnosis

paper_content:

Diagnosing analog circuits with their numerous known difficulties is a very hard problem. Digital approaches have proven to be inappropriate, and AI-based ones suffer from many problems. In this paper we present a new system, FLAMES, which uses fuzzy logic, model-based reasoning, ATMS extension, and the human expertise in an appropriate combination to go far in the treatment of this problem.
---
paper_title: Case-based diagnostic system using fuzzy neural network

paper_content:

In this paper, we describe a case-based system using fuzzy logic type neural networks for diagnosing electronic systems. We present a brief derivation of OR and AND neurons and the architecture of our system. To illustrate the effectiveness of the proposed system, we show experimental results on real data from call logs collected at the technical support centre in Ericsson Australia.
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paper_title: INSIDE: a connectionist case-based diagnostic expert system that learns incrementally

paper_content:

The authors describe a connectionist case-based diagnostic expert system that can learn while the system is being used. The system, called INSIDE (Inertial Navigation System Interactive Diagnostic Expert), was developed for Singapore Airlines to assist the technicians in diagnosing the inertial navigation system used by the airplanes. The system learns from past repair cases and adapts its knowledge base to newly solved cases without having to relearn all the old cases. >
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paper_title: A Model-Based Diagnosis System for Identifying Faulty Components in Digital Circuits

paper_content:

We describe the ideas and implementation of a model-based diagnosis system for digital circuits. Our work is based on Reiter's theory of diagnosis from first principles [14], incorporated with Hou's theory of measurements [17], to derive possible diagnoses in a fault diagnosis task. To determine the best order in which measurements are to be taken, a measurement selection strategy using the genetic algorithm (MSSGA) is proposed. A circuit description language for describing circuits hierarchically is given. An efficient propositional logic prover used for consistency checking based on the trie structure is developed [22]. An example run is given to illustrate the working of the system. Finally, a comparison with other systems is discussed, and possible extensions to our system are described.
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paper_title: Circuit diagnosis support system for electronics assembly operations

paper_content:

Abstract Diagnosis and repair operations are often major bottlenecks in electronics circuit assembly operations. Increasing board density and circuit complexity have made fault diagnosis difficult. But, with shrinking product life cycles and increasing competition, quick diagnosis and feedback is critical for cost control, process improvement, and timely product introduction. This paper describes a case-based diagnosis support system to improve the effectiveness and efficiency of circuit diagnosis in electronics assembly facilities. The system stores individual diagnostic instances rather than general rules and algorithmic procedures, and prioritizes the tests during the sequential testing process. Its knowledge base grows as new faults are detected and diagnosed by the analyzers. The system provides distributed access to multiple users, and incorporates on-line updating features that make it quick to adapt to changing circumstances. Because it is easy to install and update, this method is well-suited for real manufacturing applications. We have implemented a prototype version, and tested the approach in an actual electronics assembly environment. We describe the system's underlying principles, discuss methods to improve diagnostic effectiveness through principled test selection and sequencing, and discuss managerial implications for successful implementation.
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paper_title: Knowledge-based systems for instrumentation diagnosis, system configuration and circuit and system design

paper_content:

Abstract A number of knowledge-based systems in the electronic engineering field have been developed in the past decade. These include those that use knowledge-based techniques to diagnose instrumentation, determine system configurations and to aid circuit and system design. This paper reviews the literature on knowledge-based systems for electronic engineering applications in these areas and reports progress made in the development of a basis for realising electronic systems.
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paper_title: Pimtool, an expert system to troubleshoot computer hardware failures

paper_content:

This paper describes a tool to diagnose the cause of failure of a HP computer server. We do this by analyzing the dump of Processor Internal Memory (PIM) and maximizing the leverage of expert learning from one hardware failure situation to another. The tool is a rule-based expert system, with some nested rules which translate to decision trees. The rules were implemented using a metalanguage which was customized for the hardware failure analysis problem domain. Pimtool has been deployed to 25 users as of December 1996. We plan to expand usage to over 400 users by end of 1997. Using Pimtool, we expect to save over 15 minutes in Mean-Time-to-Repair (MTIR) per call. We have recognized that knowledge management will be a key issue in the future and are developing tools and strategies to address it.
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paper_title: AN INTELLIGENT APPROACH TO AUTOMATIC TEST EQUIPMENT

paper_content:

In diagnosing a failed system, a smart technician would choose tests to be performed based on the context of the situation. Currently, test program sets do not fault-. isolate within the context of a situation. Instead, testing follows a rigid, predetermined, fault-isolation sequence that is based on an embedded fault tree. Current test programs do not tolerate instrument failure and cannot redirect testing by incorporating new information. However, there is a new approach to automatic testing that emulates the best features of a trained technician yet, unlike the development of rule-based expert systems, does not require a trained technician to build the knowledge base. This new approach is model-based and has evolved over the last 10 years. This evolution has led to the development of several maintenance tools and an architecture for intelligent automatic test equipment (ATE). The architecture has been implemented for testing two cards from an AV-8B power supply.
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paper_title: An incremental retrieval mechanism for case-based electronic fault diagnosis

paper_content:

One problem with using CBR for diagnosis is that a full case description may not be available at the beginning of the diagnosis. The standard CBR methodology requires a detailed case description in order to perform case retrieval and this is often not practical in diagnosis. We describe two fault diagnosis tasks where many features may make up a case description but only a few features are required in an individual diagnosis. We evaluate an incremental CBR mechanism that can initiate case retrieval with a skeletal case description and will elicit extra discriminating information during the diagnostic process.
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paper_title: A CBR application: service productivity improvement by sharing experience

paper_content:

Field service is now recognized as one of the most important corporate activities in order to improve customer satisfaction and to compete successfully world-wide competition. Sharing repair experience with a state-of-the-art computer technology is a key issue to improve the productivity of field service. We have developed a diagnostic expert system, named Doctor, which employs case-based reasoning (CBR) and lists the most necessary ten service parts from a product type and some symptoms acquired from a service-request call. In this paper, we describe the Doctor system and explain how accurate and reliable product-type case-bases are generated and updated from the troubleshooting experience and the generic case base, i.e., general diagnostic knowledge. We also demonstrate the effectiveness of our system with experimental results using real repair cases. >
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paper_title: Circuit diagnosis support system for electronics assembly operations

paper_content:

Abstract Diagnosis and repair operations are often major bottlenecks in electronics circuit assembly operations. Increasing board density and circuit complexity have made fault diagnosis difficult. But, with shrinking product life cycles and increasing competition, quick diagnosis and feedback is critical for cost control, process improvement, and timely product introduction. This paper describes a case-based diagnosis support system to improve the effectiveness and efficiency of circuit diagnosis in electronics assembly facilities. The system stores individual diagnostic instances rather than general rules and algorithmic procedures, and prioritizes the tests during the sequential testing process. Its knowledge base grows as new faults are detected and diagnosed by the analyzers. The system provides distributed access to multiple users, and incorporates on-line updating features that make it quick to adapt to changing circumstances. Because it is easy to install and update, this method is well-suited for real manufacturing applications. We have implemented a prototype version, and tested the approach in an actual electronics assembly environment. We describe the system's underlying principles, discuss methods to improve diagnostic effectiveness through principled test selection and sequencing, and discuss managerial implications for successful implementation.
---
paper_title: Test and diagnosis of analog circuits: When fuzziness can lead to accuracy

paper_content:

Testing and diagnosing analog circuits is a very challenging problem. The inaccuracy of measurement and the infinite domain of possible values are the principal difficulties. AI approaches were the base of many systems which tried to overcome these problems. The first part of this paper is a state of the art of this research area. We present two fundamental approaches, model-based reasoning and qualitative reasoning, and the systems implementing them; a discussion and an evaluation of these systems are given. In the second part, we present and propose a novel approach based on fuzzy logic in order to go further in dealing with analog circuits testing and diagnosis. Tolerance is treated by means of fuzzy intervals which are more general, more efficient and of higher fidelity to represent the imprecision in its different forms than other approaches. Fuzzy intervals are also able to be semi-qualitative which is more suitable to the simulation of analog systems. We use this idea to develop a best test point finding strategy based on fuzzy probabilities and fuzzy decision-making methodology. Finally, a complete expert system which implements this approach is presented.
---
paper_title: Circuit diagnosis support system for electronics assembly operations

paper_content:

Abstract Diagnosis and repair operations are often major bottlenecks in electronics circuit assembly operations. Increasing board density and circuit complexity have made fault diagnosis difficult. But, with shrinking product life cycles and increasing competition, quick diagnosis and feedback is critical for cost control, process improvement, and timely product introduction. This paper describes a case-based diagnosis support system to improve the effectiveness and efficiency of circuit diagnosis in electronics assembly facilities. The system stores individual diagnostic instances rather than general rules and algorithmic procedures, and prioritizes the tests during the sequential testing process. Its knowledge base grows as new faults are detected and diagnosed by the analyzers. The system provides distributed access to multiple users, and incorporates on-line updating features that make it quick to adapt to changing circumstances. Because it is easy to install and update, this method is well-suited for real manufacturing applications. We have implemented a prototype version, and tested the approach in an actual electronics assembly environment. We describe the system's underlying principles, discuss methods to improve diagnostic effectiveness through principled test selection and sequencing, and discuss managerial implications for successful implementation.
---
paper_title: Research Perspectives and Case Studies in System Test and Diagnosis

paper_content:

Preface. 1. Diagnostic Inaccuracies: Approaches to Mitigate W.R. Simpson. 2.Pass/Fail Limits - The Key to Effective Diagnostic Tests H. Dill. 3. Fault Hypothesis Computations Using Fuzzy Logic T.M. Bearse, M.L. Lynch. 4. Deriving a Diagnostic Inference Model from a Test Strategy T.M. Bearse. 5. Inducing Diagnostic Inference Models from Case Data J.W. Sheppard. 6. Accurate Diagnosis Through Conflict Management J.W. Sheppard, W.R. Simpson. 7. System Level Test Process Characterization and Improvement D. Farren, et al. 8. A Standard for Test and Diagnosis J. Taylor. 9. Advanced Onboard Diagnostic System for Vehicle Management K. Keller, et al. 12. Combining Model-Based and Case-Based Expert Systems M. Ben-Bassat, et al. 11. Enhanced Sequential Diagnosis A. Biasizzo, et al. Subject Index.
---
paper_title: FLAMES: A Fuzzy Logic ATMS and Model-based Expert System for Analog Diagnosis

paper_content:

Diagnosing analog circuits with their numerous known difficulties is a very hard problem. Digital approaches have proven to be inappropriate, and AI-based ones suffer from many problems. In this paper we present a new system, FLAMES, which uses fuzzy logic, model-based reasoning, ATMS extension, and the human expertise in an appropriate combination to go far in the treatment of this problem.
---
paper_title: Model-Based Diagnosis under Real-World Constraints

paper_content:

I report on my experience over the past few years in introducing automated, model-based diagnostic technologies into industrial settings. In partic-ular, I discuss the competition that this technology has been receiving from handcrafted, rule-based diagnostic systems that has set some high standards that must be met by model-based systems before they can be viewed as viable alternatives. The battle between model-based and rule-based approaches to diagnosis has been over in the academic literature for many years, but the situation is different in industry where rule-based systems are dominant and appear to be attractive given the considerations of efficiency, embeddability, and cost effectiveness. My goal in this article is to provide a perspective on this competition and discuss a diagnostic tool, called DTOOL/CNETS, that I have been developing over the years as I tried to address the major challenges posed by rule-based systems. In particular, I discuss three major features of the developed tool that were either adopted, designed, or innovated to address these challenges: (1) its compositional modeling approach, (2) its structure-based computational approach, and (3) its ability to synthesize embeddable diagnostic systems for a variety of software and hardware platforms.
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paper_title: The use of design descriptions in automated diagnosis

paper_content:

Abstract This paper describes a device-independent diagnostic program called dart. dart differs from previous approaches to diagnosis taken in the Artificial Intelligence community in that it works directly from design descriptions rather than mycin -like symptom-fault rules. dart differs from previous approaches to diagnosis taken in the design-automation community in that it is more general and in many cases more efficient. dart uses a device-independent language for describing devices and a device-independent inference procedure for diagnosis. The resulting generality allows it to be applied to a wide class of devices ranging from digital logic to nuclear reactors. Although this generality engenders some computational overhead on small problems, it facilitates the use of multiple design descriptions and thereby makes possible combinatoric savings that more than offsets this overhead on problems of realistic size.
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paper_title: What Are Ontologies, and Why Do We Need Them?

paper_content:

This survey provides a conceptual introduction to ontologies and their role in information systems and AI. The authors also discuss how ontologies clarify the domain's structure of knowledge and enable knowledge sharing.
---
paper_title: Circuit diagnosis support system for electronics assembly operations

paper_content:

Abstract Diagnosis and repair operations are often major bottlenecks in electronics circuit assembly operations. Increasing board density and circuit complexity have made fault diagnosis difficult. But, with shrinking product life cycles and increasing competition, quick diagnosis and feedback is critical for cost control, process improvement, and timely product introduction. This paper describes a case-based diagnosis support system to improve the effectiveness and efficiency of circuit diagnosis in electronics assembly facilities. The system stores individual diagnostic instances rather than general rules and algorithmic procedures, and prioritizes the tests during the sequential testing process. Its knowledge base grows as new faults are detected and diagnosed by the analyzers. The system provides distributed access to multiple users, and incorporates on-line updating features that make it quick to adapt to changing circumstances. Because it is easy to install and update, this method is well-suited for real manufacturing applications. We have implemented a prototype version, and tested the approach in an actual electronics assembly environment. We describe the system's underlying principles, discuss methods to improve diagnostic effectiveness through principled test selection and sequencing, and discuss managerial implications for successful implementation.
---

Title: Fault diagnosis of electronic systems using intelligent techniques: a review

Section 1: Introduction
Description 1: Introduce the increasing complexity of electronic systems and the need for intelligent diagnostic techniques.

Section 2: Motivation
Description 2: Discuss the motivation behind the development of intelligent fault diagnosis techniques and the challenges faced in their industrial acceptance.

Section 3: Diagnostic Process
Description 3: Describe the steps involved in the fault diagnosis process, including fault information generation, fault hypotheses generation, and fault hypothesis discrimination.

Section 4: Traditional Approaches
Description 4: Review traditional fault diagnosis approaches such as rule-based systems and fault trees, including their applications and issues.

Section 5: Model-Based Approaches
Description 5: Discuss the various model-based approaches for fault diagnosis, including fault models, causal models, structural models, and diagnostic inference models, along with their applications and challenges.

Section 6: Machine Learning Approaches
Description 6: Explore machine learning techniques used in fault diagnosis, such as case-based reasoning, explanation-based learning, and learning knowledge from data, including their applications and issues.

Section 7: Intelligent Techniques
Description 7: Present other intelligent techniques such as fuzzy logic and artificial neural networks, covering their approaches, applications, and issues.

Section 8: Hybrid Approaches
Description 8: Discuss the combination of different techniques (e.g., model-based reasoning, case-based reasoning, fuzzy logic, neural networks) to improve fault diagnosis.

Section 9: Diagnostic Standards
Description 9: Outline the importance of IEEE standards related to AI in test and diagnostic environments, specifically AI-ESTATE.

Section 10: Commentary
Description 10: Provide a commentary on the different classes of knowledge applied to diagnosis, their advantages, disadvantages, and suitability for various application domains.

Section 11: Future Directions
Description 11: Discuss future research directions and challenges in the field of intelligent fault diagnosis of electronic systems.

Section 12: Summary
Description 12: Summarize the key points, emphasizing the need for automated diagnostic tools and the potential future developments in intelligent fault diagnosis.