Abstract

As the most important device of an Autonomous Underwater Vehicle (AUV), thrusters are one of the main sources of fault. If the thruster fault can be diagnosed in the early stage, it would give more time to guarantee the safety of an AUV. Fault feature extraction is the premise of fault diagnosis. The traditional feature calculation methods extract fault features from one domain. These methods work well in the case of high fault severity, but poorly in the case of weak fault severity. In addition, for weak faults, the fault features extracted by the traditional methods may not meet the monotonic relationship with fault severity and cannot be used in fault severity identification. Aiming at these problems, through experimental data analysis, this paper excludes the features that do not meet the law from the 52 selectable fault features in the time domain, frequency domain and time-frequency domain. Aiming at the problem that there is no useful feature in the frequency domain, a new feature calculation method is proposed, and the order of magnitude of the available feature is given, which provides concise and accurate information for subsequent fault feature fusion and fault severity identification.

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