基于概率神经网络的水轮机组水力振动故障诊断

Fault Diagnosis of Hydroturbine Hydraulic Vibration Based on Probabilistic Neural Network

故障诊断技术是水电站水轮机组安全稳定运行的关键技术之一。针对常规在线监测系统难以发现水轮机组振动故障问题,提出了一种基于概率神经网络的水轮机组故障诊断模型。该模型主要由故障样本数据预处理、样本数据归一化和概率神经网络等三个部分组成。诊断结果表明,所诊断样本与实际的故障类型基本一致,具有良好的诊断效果。

Fault diagnosis technology is one of the key technologies for the safe and stable operation of hydroelectric turbine units in hydropower plants.As the conventional online monitoring system is difficult to detect the unit vibration fault problem,a probabilistic neural network(PNN)algorithm is constructed,and the fault samples are designed for the fault parameters that have been collected.The program mainly includes three parts such as fault sample data pre-processing,sample data normalization and probabilistic neural network.The results show that the diagnostic results of the PNN model are consistent with the actual fault types,and this model has good diagnostic effects.