摘要
针对"蛟龙号"深海载人潜水器多推进器系统的故障检测与快速定位难题,将基于信度分配的模糊小脑神经网络(credit assignment-based fuzzy cerebellar model articulation controller, FCA–CMAC)应用于主元分析模型,提出一种基于主元分析(principal component analysis, PCA)的深海载人潜水器推进器系统故障诊断模型.首先,应用推进器系统正常运行的历史电流样本数据,由主元分析模型得到各推进器的电流预测值.其次,计算出故障检测统计量均方预测误差(squared prediction error, SPE),根据SPE值是否跳变,判断推进器系统有无故障发生.通过分别重构各推进器电流信号的SPE值对故障推进器进行定位和隔离.最后,通过对实际海试数据进行仿真处理说明了该算法的可行性,并通过与多层前馈神经网络(back propagation, BP)和常规小脑神经网络(cerebellar model articulation control-ler, CMAC)神经网络进行比较,说明基于FCA–CMAC神经网络的主元分析模型的优越性.
Aiming at the problem of fault detection and fault isolation in the multi-thruster system, a fault diagnosis model of thruster system in deep-sea human occupied vehicle based on principal component analysis (PCA) and credit assignment-based fuzzy cerebellar model articulation controller neural network (FCA–CMAC) is proposed. Firstly, the forecasting electric current values of thrusters are computed by using historical data measured under fault-free conditions and the PCA model. Secondly, the squared prediction error (SPE) is calculated to characterize the operational status of the thruster system. A fault can be detected when the SPE increases suddenly. Current values are reconstructed respectively to newly calculate the SPE to locate the faulty thruster. Finally, compared to BP and conventional CMAC, the method proposed is proved feasible and effective by the simulation of the actual sea trial data.
作者
程学龙
朱大奇
孙兵
陈云赛
CHENG Xue-long;ZHU Da-qi;SUN Bing;CHEN Yun-sai(Laboratory of Underwater Vehicles and Intelligent Systems, Shanghai Maritime University, Shanghai 201306, China;National Deep-Sea Center, Qingdao Shandong 266237, China)
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
2018年第12期1796-1804,共9页
Control Theory & Applications
基金
国家自然科学基金项目(51575336
U1706224)
国家重点研发计划(2017YFC0306302)资助~~
关键词
载人潜水器
主元分析
信号预测
故障检测
信号重构
故障隔离
human occupied vehicle
principal component analysis
signal forecast
fault detection
signal reconstruction
fault isolation
