摘要
为精确诊断水轮机尾水管涡带,该文提出一种基于小波包特征熵的神经网络故障诊断新方法。对采集到的尾水管压力脉动信号进行三层小波包分解,提取小波包特征熵,然后构造信号的小波包特征熵向量,并以此向量作为故障样本对三层 BP 神经网络进行训练,实现智能化故障诊断。试验结果表明训练成功的BP网络能够很好地诊断机组尾水管是否发生涡带以及涡带的严重程度,为水轮机故障诊断开辟新的途径。
To diagnose accurately vortex rope in draft tube of hydraulic turbine, this paper presents a new method of fault diagnosis based on wavelet packet and neural network. It adopts three-layer wavelet packet to decompose the monitored pressure pulsation signal of draft tube, extracts WP-CE (Wavelet Packet-Characteristic Entropy), and constructs WP-CE vectors of signals, then takes those vectors as fault samples to train three-layer BP(Back Propagation) neural network, finally realizes intelligent fault diagnosis. The practical example shows the trained BP neural network can diagnose the fault of draft tube and resort it by fault severity. This method develops a new direction for the fault diagnosis of hydraulic turbine.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2005年第4期99-102,共4页
Proceedings of the CSEE
基金
国家自然科学基金资助项目(50379015)。~~
关键词
水轮机
尾水管
故障诊断
小波包特征熵
神经网络
Hudraulic trbine
Draft tube
Pressure pulsation
WP-CE
Neural network
Fault diagnosis
