针对水下无人潜航器( unmanned underwater vehicle,UUV)推进系统的故障诊断问题,提出了一种基于自适应模糊融合的推进系统故障诊断算法.依据灰色预测理论,利用历史数据得到预测电流；同时,利用试验数据拟合控制电压与电枢电流的关系,建立了理论电流的计算模型.综合考虑控制电压变化率和电流异常时间对融合权值的影响,引入模糊理论,将理论电流和预测电流自适应地融合,最终得到融合电流.对比融合电流和实际检测电流变化趋势的异同,实现对UUV推进器状态的实时监控.在UUV推进器出现故障时,两者的差值变化反映了推进器的故障情况以及故障类别,继而为后续的系统恢复与自救提供了依据.湖试和海试过程分别验证了,所提出的算法能够有效地检测出推进器螺旋桨故障和推进器电机供电异常,且试验全程未出现“虚警”,同时,根据所得差值电流的范围准确判定了推进系统故障的类型.%An algorithm of propeller system fault diagnosis for UUV using fuzzy fusion is proposed in this paper. Firstly, the gray forecasting method was utilized to predict the forecasting current according to historical current. Simultaneously, the theoiy current model was built by fitting control voltage and armature current. Then, considering the effect of control voltage change rate and abnormity time of current on fitting weigh, fusion current was derived by fusing the forecasting current and the theory current with fuzzy theory introduced. Finally, compared fusion current to the real current detected, the state of propeller was observed in real time by the current error being the foundation for the system diagnosis and the fault classification with the fault existed. And it could be the precondition of system recovery and self-help. Lake experiment and sea trial results respectively demonstrated that the proposed algorithm is valid inde-tecting the screw propeller fault and the power supply system fault, without any false alarm, and the fault pattern has been classified precisely with the range of current error.
展开▼
