摘要
根据离心泵故障诊断的特点,提出运用小波包分解、重构技术进行特征提取,运用模糊神经网络和D-S证据理论对离心泵故障进行融合诊断的方法。首先利用小波包分析方法,将离心泵上测得的位移和加速度振动信号进行预处理,统一转换成故障征兆的特征向量值;其次,建立2层子模糊神经网络的拓扑结构,形成输入征兆与故障论域的映射关系,从而得到2层模糊神经网络的训练样本,对各网络进行成功训练后,利用模糊神经网络实现2层子网络的诊断并得到中间诊断结果;然后,将模糊神经网络诊断结果作为对各种故障模式的基本概率分配值,利用D-S证据理论,实现对子网络诊断结果的融合,从而得到最终的融合诊断结果;最后,试验分析证明了该方法的有效性。
According to the characteristics of fault diagnosis for centrifugal pump, wavelet package de-composition and reconstruction technique is used to extracting frequency band energy feature, a fusion diagnosis method is presented by using fuzzy neural network and D - S evidence theory for centrifugal pump. Firstly, according to the method of wavelet package, vibration signals of displacement and acceleration of centrifugal pump were disposed and trans- formed into feature vector value ; then, two sub-FNN structures were established, and their training samples were obtained. After two sub-FNN were trained successfully, the intermediate diagnosis results were obtained through two sub-FNN. Finally, the FNN diagnosis results were used as the basic probability distribution value to each fault mode, and the D - S evidence theory was applied, and the final fusion diagnosis results were obtained. The experimental result was to verify the method presented in this paper.
出处
《华电技术》
CAS
2009年第2期32-35,78,共5页
HUADIAN TECHNOLOGY
