BP算法的研究及在汽轮机故障诊断中的应用

运用Nguyen-Widrow方法初始化Levenberg-Marquardt算法中的变量参数,在此基础上,L-M算法在训练神经网络中的应用,减少了网络训练时权值和阈值的调整次数,加快了算法收敛速度,保证了误差为误差平面的全局最小值,并将使用N-W方法的L-M算法应用于汽轮发电机的故障诊断。经理论和实践证明该方法提高了故障诊断的准确性和可靠性,并为转动机械提供了有效的故障诊断方法。

Research on Neural Network Algorithm and Application in Fault Diagnosis

Using Nguyen-Widrow method initializes the variable parameters of the L-M algorithm, based on the application of Levenberg-Marquardt algorithm in multilayer feed-forward neural network training, reduce the number of the adjustment of the threshold value and the weight When the network training, speed up the convergence speed of BP algorithm, which ensures the global minimum of the error of plane, and apply the N-W method combined with L-M algorithm for fault diagnosis of steam turbine. The theory and practice has proved that: this method has improved the accuracy and reliability of fault diagnosis, which provides effective method for the turbo generator fault diagnosis.