The objective of this paper is to address the problem of Fault Detection and Isolation (FDI) on thrusters of an over-actuated Autonomous Underwater Vehicle (AUV) under on/off abrupt faults. The goal is pursued through Non-Linear Principal Component Analysis (NLPCA), which is the non-linear extension of the popular Principal Component Analysis (PCA). While the Fault Detection (FD) system directly exploits the model-free nature of NLPCA (data-driven approach), the Fault Isolation (FI) is achieved by properly train off-line Artificial Neural Network (ANN). The consistency and robustness of the proposed method is verified in realistic simulation.

A NLPCA hybrid approach for AUV thrusters fault detection and isolation

Fabiani, Filippo;
2016-01-01

Abstract

The objective of this paper is to address the problem of Fault Detection and Isolation (FDI) on thrusters of an over-actuated Autonomous Underwater Vehicle (AUV) under on/off abrupt faults. The goal is pursued through Non-Linear Principal Component Analysis (NLPCA), which is the non-linear extension of the popular Principal Component Analysis (PCA). While the Fault Detection (FD) system directly exploits the model-free nature of NLPCA (data-driven approach), the Fault Isolation (FI) is achieved by properly train off-line Artificial Neural Network (ANN). The consistency and robustness of the proposed method is verified in realistic simulation.
2016
978-1-5090-0658-8
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11771/25761
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