A Continuous-Time Degradation Model for Autonomous Underwater Vehicles with Data-Driven Mission Decision Rules

The article presents a methodology for assessing the mission state of the MBG-AUV, designed to support the decision to continue or abort a task in a traceable manner. The approach combines a five-state operational graph (S0–S4) with telemetry through a Markov chain, whose transition intensities are determined directly from onboard and environmental signals. The data are synchronized in UTC time, subject to quality control and unit standardization, and subsequently transformed into cumulative exposure (hazard) and risk as a function of time. For the analyzed 60 min coastal mission profile, the end-of-mission risks were RComm(T) ≈ 0.29, RHull(T) ≈ 0.011 and RPower(T) ≈ 0.006, with the first warning threshold (αₑ = 0.10) crossed after approximately 20 min at a depth of ~167 m. These values quantify the dominant contribution of acoustic communication to the overall mission risk. At the mission level, we report two complementary assessments—a weighted average (with operationally defined subsystem weights) and an assessment under the assumption of independence, along with the time of first warning, subsystem contribution ranking, and “hot” segments of the profile. The difference between the weighted mission estimate and the independence-based estimate was approximately 0.03 by the end of the mission, indicating the operational relevance of weight selection. A case study indicates that coastal missions are typically dominated by acoustic link limitations while maintaining comfortable energy and structural margins. The methodology preserves notational consistency, is straightforward to implement in ground or onboard tools, and is scalable to the full set of seven subsystems and subsequent profiles. The future work includes modeling parameter uncertainties, inter-subsystem couplings, and platform loss, as well as integration with trajectory planning to limit exposure.