Fault degree identification method for thruster of autonomous underwater vehicle using homomorphic membership function and low frequency trend prediction

Abstract

This article presents a novel thruster fault degree identification method for autonomous underwater vehicle. The novel method is developed from the fuzzy support vector domain description method, which establishes a fault identification model first, and then estimates fault degree according to the model. When establishing fault identification model for thruster based on fuzzy support vector domain description method, it is found that the relative fitting error of the model to the actual fault degree is large, making the model accuracy poor. To reduce the relative fitting error, a homomorphic membership function method is proposed. Different from fuzzy support vector domain description method, which calculates the fuzzy membership degree of fault sample in time domain, the proposed method calculates the fuzzy membership degree in log domain. On estimating thruster fault degree by fuzzy support vector domain description method, it is obtained that the estimated fault degree lags behind the actual fault degree. To shorten the lag time, a low frequency trend prediction method is proposed. Different from fuzzy support vector domain description method, which brings the fault feature extracted from the current surge speed and control voltage into the fault identification model to calculate fault degree, the proposed method firstly forward predicts surge speed and control voltage, and then takes the fault feature extracted from the predicted surge speed and control voltage into the model to acquire fault degree. The effectiveness of the proposed methods is verified by pool experiments of the experimental prototype.