一种基于粗糙集理论的容错网络实现及其在故障诊断中的应用  

Realization of a fault-tolerance neural networks based on rough sets and its application to fault diagnosis

作　　者：刘宜平[1] 沈毅[1] 童树鸿[1] 刘志言[1] 

机构地区：[1]哈尔滨工业大学,黑龙江哈尔滨150001

出　　处：《电机与控制学报》2000年第2期117-121,共5页Electric Machines and Control

基　　金：国家自然科学基金资助项目(69904004)。

摘　　要：首先利用粗糙集理论对原始数据进行约简，并按一定的原则选取多个的简。然后在每一个约简的基础之上构建一个前馈子网络，并将多个子网综合成统一的容错前馈神经网络，达到对冗余信息的综合利用。通过对相应权值的训练调节，使网络的输出更精确合理。即，当某些量测信号丢失或难以获得时，可以通过其它不包含该量测信号的的简所构成的网络来进行正确的诊断，从而在信息不完备、不精确的情况下，仍保持较好的诊断性能。最后，通过对某液体火箭发动机泄漏故障检测的仿真，表明该容错网络可以满足高可靠性诊断场所的需要。We propose a highly reliable strategy for fault diagnosis based on fault--tolerance neural networks. First, we derive some reductions from crude data based on rough sets theory and choose more than one reduction according to some criteria. Then, we build a general fault--tolerance neural networks by synthesizing each subnet which is decided by a reduction so as to make full use of the redundant information. Furthermore, we adopt BP algorithm to adjust the weights of the networks to obtain the more accurate output value. That is to say, when some measured signals are missed or difficult to obtain, we can still detect the faults correctly by using the subnets corresponding to the reductions that do not include those signals. Finally, we apply the neural networks to detect fuel leakage in a rocket engine and the simulation results illustrate the effectiveness of the approach.

关 键 词：液体火箭发动机 故障诊断 粗糙集理论 容错网络 

分 类 号：V467[航空宇航科学与技术—航空宇航制造工程]