DocumentCode :
175857
Title :
The fault diagnosis research for the underwater vehicle system based on SOFCMAC
Author :
Ting Zhu ; Daqi Zhu
Author_Institution :
Lab. of Underwater Vehicles & Intell. Syst., Shanghai Maritime Univ., Shanghai, China
fYear :
2014
fDate :
May 31 2014-June 2 2014
Firstpage :
1390
Lastpage :
1394
Abstract :
For the fault diagnosis problems of the underwater vehicle sensor systems, the solution is combined by the Principal Component Analysis (PCA) and Self-Organizing Fuzzy Cerebellar Model Articulation Controller (SOFCMAC). The signal prediction model approach based on PCA and SOFCMAC is proposed in this paper. According to the history data, it can predict the signal data in the future time using the SOFCMAC method. And the statistic, Squared Prediction Error (SPE), is introduced into the approach. According to the change of the SPE value, this model can judge whether the underwater system fault occurs. Then the linear variable reconstruction method is used to isolate the fault. The water tank experimental results show that the proposed approach is capable of detecting and isolating the fault in the vehicle sensor systems efficiently and accurately.
Keywords :
fault tolerant control; fuzzy control; fuzzy neural nets; linear systems; neurocontrollers; principal component analysis; underwater vehicles; PCA; SOFCMAC; SPE; fault detection; fault diagnosis; fault isolation; linear variable reconstruction method; principal component analysis; self-organizing fuzzy cerebellar model articulation controller; signal prediction model approach; squared prediction error; underwater vehicle system; vehicle sensor systems; Analytical models; Fault diagnosis; Neural networks; Predictive models; Principal component analysis; Sensor systems; Underwater vehicles; fault detection; fault diagnosis; fault isolation; neural network; principal component analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Decision Conference (2014 CCDC), The 26th Chinese
Conference_Location :
Changsha
Print_ISBN :
978-1-4799-3707-3
Type :
conf
DOI :
10.1109/CCDC.2014.6852384
Filename :
6852384
Link To Document :
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