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The deployment of complex autonomous underwater platforms for marine science comprises sequential steps each of which is critical to mission success. Here we present a state transition approach, in the form of a Markov chain, which models step sequence from prelaunch to operation to recovery. The aim is to identify states and state transitions presenting high risk to the vehicle and hence to the mission, based on evidence and judgment. Developing a Markov chain consists of two separate tasks. The first defines the structure that encodes event sequence. The second assigns probabilities to each possible transition. Our model comprises 11 discrete states, and includes distance-dependent underway survival statistics. Integration of the Markov model with underway survival statistics allows us to quantify success likelihood during each state and state transition, and consequently the likelihood of achieving desired mission goals. To illustrate this generic process, the fault history of the Autosub3 autonomous underwater vehicle (AUV) provides the information for different operation phases. In our proposed method, faults are discriminated according to the mission phase in which they took place.