粗糙集-遗传算法-神经网络集成分类器及其在转子故障诊断中的应用研究

Rough Set-genetic Algorithm-neural Network Compositive Classifier and Its Application in Rotor Faults Diagnosis

针对转子故障诊断问题,在综合粗糙集理论、遗传算法及神经网络学习算法各自优点的基础上,提出了一种新的粗糙集-遗传算法-神经网络(RS-GA-NN)集成分类器模型。在该模型中,利用粗糙集理论的离散和约简算法实现对样本数据的特征选取;利用神经网络实现样本特征向量与故障之间的非线性映射;利用遗传算法实现对神经网络的结构优化以使神经网络的泛化能力达到最优。利用转子故障实验台模拟了不平衡、不对中、碰摩及油膜涡动4种故障的127个样本,构建了多故障识别的RS-GA-NN集成分类器,进行了转子故障的智能诊断实验,获得了很好的效果。

The fault diagnosis problem of rotor system was aimed at, on the basis of synthesizing the advantages of Rough Set （RS） theory, Genetic Algorithm （GA） and Neural Network （NN）, a new RS--GA-NN compositive classifier was put forward. In the model, the RS was used to carry out selection of sample features; the NN was used to realize the mapping between features and fault type of sample; the GA was used to optimize the structure of NN model in order to make it to reach the best generalization. The rotor fault experimental rig was used to simulate unbalance, misalignment, rubbing and oil whirling faults, and 127 faults samples are obtained. Finally, the RS--GA-NN compositive classifier of multi-faults recognition was established, and the intelligent fault diagnosis experiment was finished, and a very satisfied result is obtained.