CAJ | 학술논문

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。
침대연기륜발전궤조진동고장진단중가측삼수난이직접반영궤조고장상태적문제，제출일충융합조조집이론화신경망락적연기륜발전궤조진동고장진단방법。결합조조집대연기륜발전궤조진동신호원시특정수거진행약간，감소용여신식。장조조집여신경망락유궤결합，용우화료적신경망락진단연기륜발전궤조진동고장。시험결과표명료소술방법적유효성，위연기륜발전궤조진동고장적쾌속진단제공료가삼고적신사로。
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.