Ronald D. Bonnell

Combining sneak circuit analysis and failure modes and effects analysis

The feasibility of integrating failure modes and effects analysis (FMEA) and sneak circuit analysis (SCA) into a comprehensive reliability analysis technique is examined especially from the perspective of automation. FMEA looks at a systems strengths and weaknesses; SCA looks for latent circuit conditions which may lead to unplanned or unexpected modes of operation. The goals of the two techniques complement each other and their combination results in a more comprehensive analysis than either technique alone can achieve. The rich collection of heuristics used in SCA can be applied to design validation and also used as guidelines at various stages of system design. At both the functional level and the component level, the combined analysis is done using the same circuit representation as for the SCA and for the FMEA, and draws on the same database.<<ETX>>

Functional reasoning in a failure modes and effects analysis (FMEA) expert system

The issue of representing the knowledge of how systems work is approached from a functional perspective. A knowledge base organized around a functional representation provides the inference procedure with a focus of attention directed toward expected goals, and guides the reasoning process in determining the effects of a systems failure modes. The functional representation described includes relationships to more detailed schemes, including numerical techniques and qualitative simulations of the causal behavior of systems. A functional representation is domain-general in that functional primitives provide a language more general than any one system being modeled. The blackboard framework is proposed as a comprehensive problem-solving architecture for integrating the functional approach with other simulation and representation techniques.<<ETX>>

Viewing Computer-Aided Failure Modes and Effects Analysis from an Artificial Intelligence Perspective

Computer-aided failure modes and effects analysis FMEA is a goal of design, manufacturing, and reliability engineers to improve the quality and reliability of systems; however, commercial implementations have utilized little or no artificial intelligence AI techniques in their design. Computer-aided FMEA is viewed from an AI perspective. First, the types of knowledges exploited by FMEA experts are discussed, and model-based methods that are applicable to intelligent automation are addressed to provide a foundation for building expert systems for design and reliability analysis. Next, existing prototype systems and techniques are characterized, according to their knowledge and reasoning strategies, to familiarize the reader with recent developments. In addition, a blackboard model of an expert system for FMEA is proposed that functionally decomposes the knowledge associated with each subfunction of the FMEA process into a set of knowledge sources.

Database design for failure modes and effects analysis

A design for a database system for storing failure modes and effects analysis data has been developed using the E-R (entity-relationship) model to describe the entity sets, relationships, and attributes to a FMEA database. The relational data model can be implemented using any commercially available DBMS (database management system). A detailed discussion of the many advantages of using such a database shows that it is a sound basis for automating the FMEA process. Its usefulness in developing an automated FMEA tool for use in a multiuser environment is also discussed. The database system can also serve as a foundation for developing the analysis component needed for a true FMEA expert system. Using a database for FMEA will also improve the quality of the analysis, and make it more meaningful and visible.<<ETX>>

Modeling semantic constraints with logic in the EARL data model

The authors present a portion of the EARL semantic data model, an augmentation of the entity-relationship model, which uses notational constructs based on propositional logic to allow a greater selection of semantic integrity constraints to be captured during the specification and design of a domain schema. The use of these logic-based constructs is shown to allow more subtle constraints to be specified, so that ambiguity can be reduced when placing constraints on the universe of discourse for database or knowledge-base applications. The notion of what are semantic constraints is discussed, and notational constructs with informal semantics are defined for the EARL model. Several examples are presented.<<ETX>>

A pedagogical approach to database design via Karnaugh maps

The study of normalization is a fundamental topic that is covered in most introductory database courses taught by departments of Computer/Electrical Engineering and Computer Science. The typical pedagogical approach to normalization presents several classical algorithms, which are based upon the application of axioms and lemmas for manipulating functional dependencies, that can be used in the process of relational decomposition and synthesis. In this paper, an augmentation to the traditional pedagogical strategy is presented for introducing students to normalization and relational synthesis concepts. This augmentation transforms semantic concepts into Boolean form that can be easily manipulated with the Karnaugh map. The Karnaugh map provides an especially useful method for illustrating the process of determining the candidate keys of a relation, as well as simplifying the mechanics of manipulating functional dependencies that are required for database decomposition and synthesis. Moreover, students find the Karnaugh-map-based techniques faster for most calculations, as well as easier to apply than conventional algorithms, since most engineering students are more familiar with combinatorial Boolean algebra than the algebra of functional relations.

A blackboard model of an expert system for failure mode and effects analysis

The design of an expert system to assist in performing a failure mode and effects analysis (FMEA) is approached from a knowledge-use-level perspective to provide a thorough understanding of the problem and insight into the knowledge and expertise needed to automate the FMEA process. A blackboard model is a conceptual model that provides the organizational principles required for the design of an expert system without actually specifying its realization. In the blackboard model of an intelligent FMEA, the system is functionally decomposed into a set of knowledge sources, each containing the knowledge associated with a subfunction of the FMEA process. The conceptual model derived can be used to evaluate attempts to automate the FMEA process, and it can serve as the foundation for further research into automating the FMEA process. An example is presented illustrating the interaction among the knowledge sources in the blackboard model to construct a FMEA for a domestic hot water heater.<<ETX>>

A framework for constructing visual knowledge specifications in acquiring organizational knowledge

Most work in knowledge acquisition assumes that there is a single problem-solving task for which the knowledge base being constructed through acquisition will be applied. However, there is growing interest in a class of knowledge-based systems, referred to as organizational knowledge systems , where it cannot be assumed that there is a single task model or problem-solving method for which the knowledge is to be acquired. This paper discusses this type of knowledge-based system, the factors in organizational problem-solving which mitigate its existence, and presents a framework for knowledge acquisition in this environment. The acquisition framework, known as Conceptual Abstraction , has its basis in semantic data modeling, model-based KA, and visual programming. It has been effectively applied in the specification of knowledge schemata for organizational knowledge bases. The paper discusses the underlying model for abstraction and the associated abstraction mechanisms of the Conceptual Abstraction approach, and illustrates their use through example.

EDICT-an enhanced relational data dictionary: architecture and example

An approach is described for enhancing an integrated relational data dictionary in order to capture and faithfully present this high-level enterprise schema in a form which can be incorporated directly into the relational database system. The approach, referred to as EDICT, allows for the specification of the enterprise schema for a database as an extension of the data dictionary by storing the schema as a set of normalized tables. In addition, it facilitates more effective use of existing data dictionaries by providing an extended metaschema which describes the structure and use of the dictionary itself.<<ETX>>

An intelligent control architecture for expert process control

An intelligent control system architecture is proposed that employs multiple knowledge sources including procedural algorithms, rule-based methods, reasoning mechanisms and process knowledge for complex process control. The architecture is based on the blackboard control model which enables expert systems to flexibly select their own control strategies at different levels of abstraction for multitask problem-solving in process control. Control knowledge sources and a control blackboard are included so that the control plan can be modified dynamically according to the state of the process. Expert control systems can improve their behavior dynamically by adapting to an environment with large uncertainty or contingencies.<<ETX>>