Krzysztof W. Przytula

Reasoning Framework for Diagnosis and Prognosis

This paper describes a general-purpose probabilistic framework for reasoning in diagnosis and prognosis. The framework provides a mathematically rigorous way of handling uncertainty, which is often present in diagnosis and is inherent to prognosis. It is based on an extension of Bayesian network models and Bayesian inference. It coherently integrates multiple sources of evidence in diagnosis and prognosis, including component usage, environmental conditions of operation as well as component health and health trends. The framework has been applied to diagnosis of very complex transportation and aviation systems and to prognosis of electromechanical and electronic subsystems in aviation.

Evaluation of Bayesian networks used for diagnostics

Bayesian networks have been very useful as models for computerized diagnostic assistants, as evidenced by numerous citations in the literature. However. a number of important practical problems in the application of Bayesian networks to diagnostics have still not been properly addressed. One of these is the evaluation of Bayesian network models. The quality of a model determines the quality of diagnostic recommendations obtained using that model. Thus, comprehensive analysis and evaluation of Bayesian models provides a fm basis for estimation of performance of diagnostic tools based on these models. Our approach to Bayesian network evaluation relies on the use of Monte Carlo simulation and the efficient visualization of simulation results. This technique allows us to identify the critical elements of Bayesian models that are responsible for incorrect diagnosis. In this way we can point to components that lack strong observations and therefore cannot be diagnosed convincingly. We can identify strongly coupled components that implicate each other and therefore cannot be effectively separated in diagnosis. We can also identify components whose failures are consistently misinterpreted as failures of other components. TABLE OF CONTENTS

An Implementation of Prognosis with Dynamic Bayesian Networks

We present a probabilistic approach to reasoning in diagnosis and prognosis. The approach represents a mathematically rigorous way of handling uncertainty, which is often present in diagnosis, but inherent to prognosis. The approach is based on a novel form of layered dynamic Bayesian network models, which is used to perform Bayesian inference. It coherently integrates evidence on component usage, environmental conditions of operation, as well as component health history. The approach has been tested on several examples of health prognosis for electromechanical and electronic subsystems in aviation.

Development of Bayesian diagnostic models using troubleshooting flow diagrams

Bayesian networks have recently become a modeling technique of choice for development of flexible, accurate, and complex diagnostic systems. These characteristics are obtained, however, at the significant cost of data and expert knowledge. It is often the case that a troubleshooting flow diagram, the most popular way of representing troubleshooting procedures, is already available for the system and can be used as a starting point for design of the Bayesian network. It turns out that conversion of the flow diagram into a Bayesian network is very similar to conversion into a diagnostic case base. We compare the case base and Bayesian network obtained by conversion with the original flow diagram, from the point of view of their diagnostic performance. We also describe a procedure for cost and time efficient enhancement of the original case base and Bayesian network. We discuss the sequencing algorithms necessary to use case bases and Bayesian networks in troubleshooting, with particular attention to decision tree and Value of Information based sequencing. We have used our design procedure in development of several complex diagnostic systems for troubleshooting of satellites, vehicles, and test equipment.

Bayesian networks for satellite payload testing

Satellite payloads are fast increasing in complexity, resulting in commensurate growth in cost of manufacturing and operation. A need exists for a software tool, which would assist engineers in production and operation of satellite systems. We have designed and implemented a software tool, which performs part of this task. The tool aids a test engineer in debugging satellite payloads during system testing. At this stage of satellite integration and testing both the tested payload and the testing equipment represent complicated systems consisting of a very large number of components and devices. When an error is detected during execution of a test procedure, the tool presents to the engineer a ranked list of potential sources of the error and a list of recommended further tests. The engineer decides this on this basis if to perform some of the recommended additional test or replace the suspect component. The tool has been installed in payload testing facility. The tool is based on Bayesian networks, a graphical method of representing uncertainty in terms of probabilistic influences. The Bayesian network was configured using detailed flow diagrams of testing procedures and block diagrams of the payload and testing hardware. The conditional and prior probability values were initially obtained from experts and refined in later stages of design. The Bayesian network provided a very informative model of the payload and testing equipment and inspired many new ideas regarding the future test procedures and testing equipment configurations. The tool is the first step in developing a family of tools for various phases of satellite integration and operation.