@inproceedings{6d7d5ecf6445413baa3fdad55f6c79c9,
title = "Research on the sensors condition monitoring method for AUV",
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.",
keywords = "Autonomous underwater vehicles(AUV), Condition monitoring, RBF Neural Network, Sensor fault",
author = "Yujia Wang and Jie Zhao and Mingjun Zhang",
year = "2008",
doi = "10.1007/978-3-540-88513-9\_46",
language = "英语",
isbn = "3540885129",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
number = "PART 1",
pages = "427--436",
booktitle = "Intelligent Robotics and Applications - First International Conference, ICIRA 2008, Proceedings",
address = "德国",
edition = "PART 1",
note = "1st International Conference on Intelligent Robotics and Applications, ICIRA 2008 ; Conference date: 15-10-2008 Through 17-10-2008",
}