Abstract
A modeling method for diagnosing the faults and restoring the uncertain signals of sensors is proposed, which uses a combined Radial Basis Function (RBF) neural network and resolves the problem of multi-sensors coupling of Autonomous Underwater Vehicle (AUV). In the common controller system, each sensor has a RBF identification network for its own, and by comparing the dispersion of actual output and the model output with an experiential threshold on a prescribed period of time, it can detect the fault occurring on the sensor being monitored. All sensors are classified according to the signal comparability, so the signal of the fault sensor can be corrected by the RBF restoration network, which consists of the sensors with similar output. The results of the computer simulation by actual experiment data of a certain AUV shows that the combined RBF network used in the multi-sensors fault diagnosis and signal restoration is effective and proves that the condition monitoring model proposed in this article is feasible.
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Wang, Y., Zhao, J., Zhang, M. (2008). Research on the Sensors Condition Monitoring Method for AUV. In: Xiong, C., Huang, Y., Xiong, Y., Liu, H. (eds) Intelligent Robotics and Applications. ICIRA 2008. Lecture Notes in Computer Science(), vol 5314. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88513-9_46
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DOI: https://doi.org/10.1007/978-3-540-88513-9_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88512-2
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