基于小波包和RBF神经网络的压电加速度传感器故障诊断  

DIAGNOSIS OF PIEZOELECTRIC ACCELERATION SENSOR FAULT BASED ON WAVELET PACKET AND RBF NEURAL NETWORK

作　　者：杜菲[1] 马天兵[1,2] 

机构地区：[1]安徽理工大学机械工程学院,安徽淮南232001 [2]南京航空航天大学机械结构力学及控制国家重点实验室,江苏南京210016

出　　处：《井冈山大学学报（自然科学版）》2013年第3期54-57,共4页Journal of Jinggangshan University (Natural Science)

基　　金：安徽省高校优秀青年人才基金重点项目(2012SQRL045ZD)

摘　　要：根据压电加速度传感器故障的特点,提出运用小波包变换和RBF神经网络的故障诊断方法。首先运用小波包分解和重构原理将传感器输出信号分解到不同频段中,提取每个频段的能量作为状态监测的特征向量,作为RBF网络的输入,然后利用最佳的RBF神经网络进行压电传感器故障分类。实验结果表明该方法具有良好的非线性跟踪能力,较高的诊断准确率。According to the character of piezoelectric acceleration sensor fault, a new diagnosis method based on wavelet packet transform and RBF neural network is proposed to detect and identify sensor fault. The sensor fault signals are decomposed in different frequency bands by wavelet packet decomposition and reconstruction, and the energy of every band is used as the.eigenvector of condition monitoring as well as input of RBF （Radial Basis Function） neural-network. The classification of sensor fault is conducted by using the best RBF neural network. Experiment results prove that the method has good tracking ability of nonlinear system and higher diagnosis accuracy.

关 键 词：压电加速度传感器 小波包变换 神经网络 故障诊断 

分 类 号：TP206[自动化与计算机技术—检测技术与自动化装置]