Model-Based Diagnosis of Real-Time Systems: Robustness Against Varying Latency, Clock Drift, and Out-of-Order Observations

Abstract

Online fault diagnosis techniques are a key enabler of effective failure mitigation. For real-time systems, the problem of identifying faults is aggravated by timing imprecisions such as varying latency between events and their observation. This paper tackles the challenge of diagnosing faults based on partial observations which are subject to timing imprecisions and potentially made out-of-order due to latency. In this paper, we develop a theory of robust real-time diagnosis importing well-established notions from timed automata theory and the diagnosis of discrete event systems. The theory itself enables a foundational understanding and investigation of the problem and its intricacies. Based on this theory, we further devise an online diagnosis algorithm consuming observations incrementally as they are made and enabling diagnosis, whenever possible, within a bounded worst-case delay. We prove the correctness of the algorithm and its properties with respect to the theory. Aiming at practical feasibility, we also show how to obtain sound but not necessarily complete diagnosis results with space and time requirements bounded by the size of the system model and independent of the number of observations. Finally, using a prototypical implementation, we report on first empirical results obtained by simulation of a small excerpt of an industrial automation example.