融合粗糙集与神经网络的燃气轮发电机组振动故障诊断方法

针对燃气轮发电机组振动故障诊断中可测参数难以直接反映机组故障状态的问题,提出一种融合粗糙集理论和神经网络的燃气轮发电机组振动故障诊断方法。结合粗糙集对燃气轮发电机组振动信号原始特征数据进行约简,减少冗余信息。将粗糙集与神经网络有机结合,用优化了的神经网络诊断燃气轮发电机组振动故障。试验结果表明了所述方法的有效性,为燃气轮发电机组振动故障的快速诊断提供了可参考的新思路。

Fault diagnosis of gas turbine generator set by combination of rough sets and neural network

In view of the problem that fault diagnosis for gas turbine vibration generator set parameters is difficult to reflect the state of unit fault directly, a fusion of rough set and neural network for gas turbine generator set vibration fault diagnosis is presented. Rough sets theory is applied in reduction of the original features of the vibration signal characteristic value data to remove unnecessary attributes. An optimized neural network structure which is used to fault diagnosis of gas turbine generator set is established based on rough sets. The experimental results show that the method is effective and provides a new idea for gas turbine generator set vibration fault diagnosis.