Details of Award
NERC Reference : NE/F01256X/1
Automated Diagnosis for Fault Detection, Identification and Recovery in Autosub6000
Grant Award
- Principal Investigator:
- Dr RW Dearden, University of Birmingham, School of Computer Science
- Co-Investigator:
- Professor JL Wyatt, University of Birmingham, School of Computer Science
- Grant held at:
- University of Birmingham, School of Computer Science
- Science Area:
- Marine
- Overall Classification:
- Marine
- ENRIs:
- Pollution and Waste
- Natural Resource Management
- Global Change
- Environmental Risks and Hazards
- Biodiversity
- Science Topics:
- Technol. for Environ. Appl.
- Abstract:
- For the past 25 years autonomous underwater vehicles or robots (commonly called AUVs) have been improving. AUVs are now used by a wide range of ocean scientists to understand the way that our oceans work, and how the way they work is changing. AUVs allow scientists to access parts of the oceans that were otherwise inaccessible to them (e.g. under ice shelves, or for long searches on the deep ocean floor). However, the environments in which these vehicles operate are extremely harsh. Therefore even the most advanced AUVs are sometimes permanently lost because of hardware failures caused by the leakage, by accidental damage during oepration, or due to minor manufacturing errors. In this work we will bring techniques developed for the space programme to useful application in AUVs. The idea is that new kinds of software can be used by the AUV so that it can diagnose what is wrong with itself, and can decide to abort its mission safely, or alternatively to take remedial action (such as switching to a back-up system) that will help it to recover and carry on its mission. This kind of software is sometimes called automated diagnosis software. For it to work the vehicle must have a model of how its critical internal systems (such as the power system) work. When a fault occurs the program can not only detect the fault (e.g. power loss in a sub-system), but reason about the likely causes. Once the cause of a fault has been identified (perhaps there is a leak, or perhaps the vehicle has a faulty connection) the correct remedial action can be taken (perhaps the vehicle should abort the mission and return to a safe place). This kind of self-diagnosis in autonomous underwater vehicles will also be useful in applications for the military (e.g. AUVs doing port surveillance) and the oil industry (inspecting well-heads or pipes). Indeed in the long term these technologies will benefit almost anyone using complex hardware in remote locations with poor communication, so that faults cannot be directly diagnosed by a human may be dealt with in some way. As instrumentation and equipment used in remote and extreme environments grows more complex both the need for automated diagnosis, and the facilities to support it, grow greater. In this work we will develop and integrate state of the art automated fault detection and diagnosis techniques in the British Autosub6000, a deep diving AUV developed at the National Oceanography Centre in Southampton, and used to support important science missions --- such as taking measurements that tell us about the rate of climate change and its effects. Our work will increase the reliability of this platform and thus make it more effective in playing its role in helping us to understand how the Earth is changing.
- NERC Reference:
- NE/F01256X/1
- Grant Stage:
- Completed
- Scheme:
- Directed (Research Programmes)
- Grant Status:
- Closed
- Programme:
- SOFI
This grant award has a total value of £317,687
FDAB - Financial Details (Award breakdown by headings)
| DI - Other Costs | Indirect - Indirect Costs | DA - Investigators | DI - Staff | DA - Estate Costs | DI - T&S |
|---|---|---|---|---|---|
| £6,788 | £93,339 | £25,832 | £106,404 | £59,910 | £25,415 |
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