Sensor fault detection and isolation of an autonomous underwater vehicle using partial kernel PCA
المؤلف | Navi, M. |
المؤلف | Davoodi, M. |
المؤلف | Meskin, Nader |
تاريخ الإتاحة | 2022-04-14T08:45:42Z |
تاريخ النشر | 2015 |
اسم المنشور | 2015 IEEE Conference on Prognostics and Health Management: Enhancing Safety, Efficiency, Availability, and Effectiveness of Systems Through PHAf Technology and Application, PHM 2015 |
المصدر | Scopus |
المعرّف | http://dx.doi.org/10.1109/ICPHM.2015.7245022 |
الملخص | In this paper, partial kernel principal component analysis (PKPCA) is studied for sensor fault detection and isolation (FDI) of an autonomous underwater vehicle (AUV). Principal component analysis (PCA) is an effective health monitoring tool which can achieve acceptable results only for linear processes. In the case of nonlinear systems such as autonomous underwater vehicles, kernel PCA approach can be used which leads to more accurate health monitoring and fault diagnosis. In order to achieve fault isolation, partial KPCA is proposed where a set of residual signals is generated based on the parity relation concept. The simulation studies demonstrate that using the proposed methodology, the occurrence of sensor faults in the nonlinear six degrees of freedom (DOF) model of an AUV can be effectively detected and isolated. 2015 IEEE. |
راعي المشروع | Qatar National Research Fund |
اللغة | en |
الناشر | Institute of Electrical and Electronics Engineers Inc. |
الموضوع | Data handling Degrees of freedom (mechanics) Electric fault currents Fault detection Health Principal component analysis Systems engineering Vehicles Autonomous underwater vehicles (AUV) Fault detection and isolation Health monitoring Kernel principal component analyses (KPCA) Sensor fault detection Sensor fault detection and isolations (FDI) Simulation studies Six degrees of freedom Autonomous underwater vehicles |
النوع | Conference Paper |
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