NDLI: The fault diagnosis research for the underwater vehicle system based on SOFCMAC

Content Provider	IEEE Xplore Digital Library
Author	Ting Zhu Daqi Zhu
Copyright Year	2014
Description	Author affiliation: Lab. of Underwater Vehicles & Intell. Syst., Shanghai Maritime Univ., Shanghai, China (Ting Zhu; Daqi Zhu)
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.
Starting Page	1390
Ending Page	1394
File Size	176979
Page Count	5
File Format	PDF
ISBN	9781479937073
e-ISBN	9781479937080
DOI	10.1109/CCDC.2014.6852384
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-31
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault diagnosis Analytical models fault diagnosis Neural networks fault detection Predictive models Sensor systems fault isolation neural network Underwater vehicles principal component analysis Principal component analysis
Content Type	Text
Resource Type	Article

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