Intelligent Condition Monitoring Systems for an AUV Robot

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This paper discuses intelligent techniques used to monitor and correct operational abnormalities in Autonomous Underwater Vehicles. Neural Networks are usually utilised in the diagnosis section, while Fuzzy Logic is implemented in the prognosis and remedy sections. The performance of an AUV’s sub-system has a great affect on the overall success of the vehicle. Once a sub-system becomes faulty, the various components associated with the control of the AUV may get influenced, which can degrade the overall performance of the integrated system or make it invalid altogether, [1]. Such failures may result in large amounts of wasted time, loss of data and increases in mission costs.

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Edited by:

Wen Jin

Pages:

1159-1164

Citation:

A. P. Anvar et al., "Intelligent Condition Monitoring Systems for an AUV Robot", Applied Mechanics and Materials, Vols. 152-154, pp. 1159-1164, 2012

Online since:

January 2012

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$38.00

[1] Bo, Y, Yongyuan, Q & Yan, C 2006, A method for fault detection and isolation in the integrated navigation system for UAV, Measurement Science and Technology, vol. 17, pp.1522-1528.

[2] Brown, K, Hamilton, K, Lane, D & Taylor, N 2001 Fault Diagnosis on Autonomous Robotic Vehicles with RECOVERY: An Integrated Heterogeneous-Knowledge Approach, International Conference on Robotics & Automation, pp.3232-3236.

DOI: https://doi.org/10.1109/robot.2001.933116

[3] Chen, X, Xu, Y, Wan, L & Li, Y 2010, Sensor Fault Diagnosis for Autonomous Underwater Vehicle, 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, Harbin, China, pp.2918-2923.

DOI: https://doi.org/10.1109/fskd.2010.5569278

[4] Han, Y & Song, YH 2002, Condition Monitoring Techniques for Electrical Equipment: A Literature Survey, IEEE power engineering review, vol . 22, p.59.

[5] Healy, A. J, 1992, A Network Approach to Failure Diagnostics for Underwater Vehicles, Department of Mechanical Engineering, Naval Postgraduate School, California, USA, pp.131-134.

[6] Sotelo, M. A, Bergasa, L. M, Flores, R, Ocana, M, Doussin, M, Magdalena, L, Kalwa, J, Madson, A. L, Perrier, M, Roland, D, Corigliano, P 2003, ADVOCATE II: Advanced Onboard diagnosis and Control of Autonomous Systems II, Information Processing and Management of Uncertainty, Europe, 303-313.

DOI: https://doi.org/10.1007/978-3-540-45210-2_28