基于小波包和Elman神经网络的液压泵故障诊断

针对液压泵出口故障检测信号信噪比低、难以进行故障特征提取的特点,及 传统的BP网络进行故障诊断时网络学习具有收敛速度慢和学习、记忆不稳定的缺陷,提出了 一种将小波包变换和改进Elman神经网络相结合,进行液压泵故障诊断的新方法.利用具有 紧支结构的小波函数对信号进行分解,削减小波系数以滤除信号中的噪声;单支重构以有效 提取各频带的故障特征,并以频带能量作为识别故障的特征向量;应用改进的Elman神经网 络建立从特征向量到故障模式之间的映射,实现液压泵故障分类.试验结果表明,采用小波 包和改进Elman神经网络相结合的方法可有效的实现液压泵故障的诊断. 

Fault diagnosis based on wavelet package and Elman neural network for a hydraulic pump

Considering the low signal-to-noise,faint failure characteristics of hydraulic pump,and slow convergence speed and the instability of BP network,a new fault diagnosis method based on the wavelet package analysis and Elman neural network is presented.The wavelet package analysis is adopted to eliminate the noise in the actual signals and to extract the fault characteristics.Through signal decomposition and single reconfiguration with wavelet package,the noise can be eliminated from signals to strengthen the failure signal and to extract fault feature in every frequency bands effectively.Energy of various frequency bands acting as the fault feature vector is input into the improved Elman neural network to realize the mapping between the feature vector and the fault mode.The experiment results verified the effectiveness of the proposed method in the hydraulic pump fault diagnosis.