Fault tolerance considerations for long endurance AUVs

Abstract

Autonomous Underwater Vehicles (AUV) work in a harsh and uncertain environment which imposes challenges on their energy, navigation, and communications. Given the environment, there is little bandwidth to communicate solutions to an on-board fault or failure. The AUV application discussed is for Naval Mine Countermeasures (NMCM) survey and minehunting missions. For such missions, operational availability, reliability demands, and system safety are of high importance. To address this, an on-board Fault Detection, Isolation and Recovery (FDIR) system is provided by the manufacturer for basic faults like slow leaks, over-depth, and time-outs due to unreceived operator commands. With that, most AUVs can implement a scripted mission but are generally unable to recover from more complex failures like low energy, or reduced functionality in hydroplanes. These two cases are presented here as implemented examples. The examples show that an autonomous on-board recovery system could be devised and implemented for timely recovery from these types of failures. With such measures, the AUV can be adaptive and as fault tolerant as possible to unexpected changes in itself, the environment and the mission. The recovery employed machine learning to gain insight into the best solution for a specific failure and the reason for failure from observations on faults/failures. Further, dynamic Bayesian networks (DBN) are proposed as a novel FDIR approach towards AUV reliability for long endurance NMCM missions. DBN are suited to address partial observability, uncertainties inherent in the AUV subsystems' evolution, and the subsystems' interaction with the harsh and uncertain environment. This makes advanced reactive and preventive fault/failure recovery possible.