The architecture of an advanced fault detection and diagnosis (FDD) system is described and applied with an Autonomous Underwater Vehicle (AUV). The architecture aims to provide a more capable system that does not require dedicated sensors for each fault, can diagnose previously unforeseen failures and failures with cause-effect patterns across different subsystems. It also lays the foundations for incipient fault detection and condition-based maintenance schemes. A model of relationships is used as an ontology to describe the connected set of electrical, mechanical, hydraulic, and computing components that make up the vehicle, down to the level of least replaceable unit in the field. The architecture uses a variety of domain dependent diagnostic tools (rulebase, model-based methods) and domain independent tools (correlator, topology analyzer, watcher) to first detect and then diagnose the location of faults. Tools nominate components, so that a rank order of most likely candidates can be generated. This modular approach allows existing proven FDD methods (e.g., vibration analysis, FMEA) to be incorporated and to add confidence to the conclusions. Illustrative performance is provided working in real time during deployments with the RAUVER hover capable AUV as an example of the class of automated system to which this approach is applicable. © 2007 Wiley Periodicals, Inc.