Vibration Diagnosis Method Based on Wavelet Analysis and Neural Network for Turbine-generator
The turbine-generator plays a crucial rule in modern industrial plant. The risk of turbine-generator set failure can be remarkably reduced if normal service condition can be arranged in advance. An effective approach based on wavelet neural network is presented for vibration signal analysis and fault diagnosis. The wavelet transform exhibits not only more comprehensive results, but also delivers a variety of possible explanations to the investigated problem. The main advantage of wavelet transform for signal analysis is that the wavelet coefficients are obtained by correlating vibration signal with the wavelet basis functions so that all possible fault patterns can be displayed by time-scale results. The feature vector obtained from wavelet transform coefficients are presented as input vector for neural network. The improved training algorithm is used to fulfill network training process and parameter initialization. From the output values of the neural network, the fault pattern is identified in accordance with the predefined fault feature vectors, which are obtained from practical experience. At the meantime, the convergence property of wavelet network for fault diagnosis is discussed. The experiment results demonstrate that the proposed method is effective and accurate.
Turbine-generator wavelet transform neural network fault diagnosis vibration signal parameter initialization
Pang Peilin Ding Guangbin
Hebei University of Engineering, Handan 056038, China
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
5234-5237
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）