Maintaining diagnostic truth with information flow models

Most diagnostic systems today, whether applying a static test strategy or generating the strategy incrementally, assume a fixed configuration for the system being tested. Complex systems, including communications networks and adaptive control systems, may undergo many configuration changes during normal operation and test. In this paper, we present an approach to modeling system dynamics and coupling the dynamics to a diagnostic model for on-line test and diagnosis. We focus on the problem of maintaining current truth values as the diagnostic context changes and provide efficient algorithms that can be readily incorporated into information flow model-based diagnostic systems