基于小波神经网络的汽轮发电机组振动故障预测的研究  

Research on Vibration Fault Forecast of Turbo-generator Unit based on Wavelet Neural Network

作　　者：杨崴崴[1] 缪思恩[1] 盛锴[1] 

机构地区：[1]浙江大学机能学院

出　　处：《电站系统工程》2007年第5期29-30,33,共3页Power System Engineering

摘　　要：简述了小波神经网络的基础理论和算法,论述了该方法在汽轮发电机组的振动故障预测中的应用。小波神经元的良好局部特性和多分辨率学习实现了与信号的良好匹配,使得小波神经网络有更强的自适应能力、更快的收敛速度和更高的预报精度。仿真和实验结果表明,预报结果具有良好精度。After a brief description of the fundamental theory and arithmetic of wavelet neural network,te memthod of its application to vibration fault forecast of steam turbine is discussed. With the good partial characteristic and distinguish rate learning, wavelet neural network matches commendably the signal, and has stronger self adaptation ability, more sooner convergence rate and higher forecast accuracy. Simulation and experimental results show that the forecast accuracy of wavelet neural network may meet industrial demand.

关 键 词：小波神经网络 汽轮机 振动峰-峰值 故障预测 

分 类 号：TK268.1[动力工程及工程热物理—动力机械及工程]