Abstract
In order to improve the precision and reliability of the autonomous underwater vehicle (AUV) inertial navigation system, a redundant inertial measurement unit (RIMU) based on micro electromechanical system (MEMS) inertial sensors has been designed, then use support vector machine theory (SVM),construct multi-fault classifier training and combine three-step search parameter optimization method,to achieve rapid, automatic fault detection and isolation (FDI). With Monte Carlo simulation and experimental analysis, SVM method has more obvious advantages than conventional Generalized Likelihood ratio Test (GLT) on false alarm rate, undetected rate and correct isolation rate for common fault sources of RIMU, and can detect and identify the type and number of failure more effectively on redundant systems, and provide a guarantee for fault sensors isolation.
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