Abstract-Wear and tear from sustained operations cause systems to degrade and develop faults. Online fault diagnosis schemes are necessary to ensure safe operation and avoid catastrophic situations, but centralized diagnosis approaches have large memory and communication requirements, scale poorly, and create single points of failure. To overcome these problems, we propose an online, distributed, model-based diagnosis scheme for isolating abrupt faults in large continuous systems. This paper presents two algorithms for designing the local diagnosers and analyzes their time and space complexity. The first algorithm assumes the subsystem structure is known and constructs a local diagnoser for each subsystem. The second algorithm creates a partition structure and local diagnosers simultaneously. We demonstrate the effectiveness of our approach by applying it to the Advanced Water Recovery System developed at the NASA Johnson Space Center.Note to Practitioners-Fault detection, isolation, and recovery approaches are important for maintaining performance and safety in large safety-critical systems, such as the Advanced Life Support (ALS) System for future long-duration NASA manned missions that we present in this paper. These systems consist of a number of complex, interacting, spatially-distributed subsystems. Centralized model-based diagnosis approaches are expensive in memory and communication requirements, and they create single points of failure. Previous distributed diagnosis approaches apply to discrete event system models, but these approaches become computationally intractable when applied to complex continuous systems. This paper develops a systematic modelbased approach to distributing the diagnosis task by designing multiple diagnosers that operate independently and generate globally correct diagnoses. We present a complete approach that includes a topological modeling scheme for constructing the dynamic system models, algorithms for constructing the distributed diagnosers, and a systematic methodology for deriving efficient subsystem diagnosis using these diagnosers. We then demonstrate the applicability of this approach to the ALS system.