Realization of a fault-tolerance neural networks based on rough sets and its application to fault diagnosis
Zhiyan Liu · Dianji yu kongzhi xuebao · 2000
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.