Title :
Fault diagnosis based on Grey Dynamic Prediction for AUV sensor
Author :
Bian, Xinqian ; Chen, Tao ; Yan, Zheping ; Zhao, Dehui ; Yu, Guang
Author_Institution :
Coll. of Autom., Harbin Eng. Univ., Harbin
Abstract :
Grey dynamic prediction (GDP) based sensor fault diagnosis for autonomous underwater vehicle (AUV) is proposed in this paper. This method can solve the problems of short information, strong uncertainty and real-time requirement. The principle of GDP and its practical steps for sensor fault diagnosis are introduced in detail. The simulation research is carried out for four typical fault modes of AUV sensor. The simulation result shows that the method can diagnose the sensor faults fast and accurately, and can recover the signal after faults happening in a period of time.
Keywords :
bathymetry; fault diagnosis; underwater vehicles; autonomous underwater vehicle; grey dynamic prediction; sensor fault diagnosis; Differential equations; Economic indicators; Fault detection; Fault diagnosis; Neural networks; Sensor phenomena and characterization; State estimation; Uncertainty; Underwater vehicles; Vehicle dynamics;
Conference_Titel :
Industrial Technology, 2009. ICIT 2009. IEEE International Conference on
Conference_Location :
Gippsland, VIC
Print_ISBN :
978-1-4244-3506-7
Electronic_ISBN :
978-1-4244-3507-4
DOI :
10.1109/ICIT.2009.4939648
