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Automated Fault Diagnosis for an Autonomous Underwater Vehicle

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2 Author(s)
Richard Dearden ; School of Computer Science, University of Birmingham, Edgbaston, Birmingham, West Midlands, U.K. ; Juhan Ernits

This paper reports our results in using a discrete fault diagnosis system Livingstone 2 (L2), onboard an autonomous underwater vehicle (AUV) Autosub 6000. Due to the difficulty of communicating between an AUV and its operators, AUVs can benefit particularly from increased autonomy, of which fault diagnosis is a part. However, they are also restricted in their power consumption. We show that a discrete diagnosis system can detect and identify a number of faults that would threaten the health of an AUV, while also being sufficiently lightweight computationally to be deployed onboard the vehicle. Since AUVs also often have their missions designed just before deployment in response to data from previous missions, a diagnosis system that monitors the software as well as the hardware of the system is also very useful. We show how a software diagnosis model can be built automatically that can be integrated with the hardware model to diagnose the complete system. We show empirically that on Autosub 6000 this allows us to diagnose real vehicle faults that could potentially lead to the loss of the vehicle.

Published in:

IEEE Journal of Oceanic Engineering  (Volume:38 ,  Issue: 3 )