摘要
提出了一种基于小波变换的转子故障信号能量特征自动提取方法,受倍频分析思想的启发,运用尺度变换对原始时间信号重采样,将重采样后的信号进行小波变换,并统一分解到给定层上,从而获取信号的频带特征.该方法能消除转子转速和采样频率对小波分解频带分布的影响,提取的频带能量特征具有统一的物理意义.在ZL-3多功能转子模拟试验台上模拟了不平衡、不对中、碰摩及油膜涡动4种转子常见故障的128个样本,应用本文方法进行小波分析特征提取,并构造集成神经网络诊断模型进行诊断实验,结果表明了本文方法的有效性和正确性.
A new method based on wavelet transform is proposed to extract rotor fault signal energy features automatically. The method is motivated by multiple-frequency analysis and the scale transform theory is applied to resample the original signal at certain time interval. The re-sampled signal is decomposed into a predefined wavelet transform, and the energy features of the frequency band are acquired. It can eliminate the effect of layer with rotor speed and sampling frequency on the distribution of wavelet analysis frequency band energy. Energy features of the frequency band have unified physical meanings. In a simulation setup of the ZL-3 multi-function rotor, we simulate 128 samples including 4 kinds of common rotor faults, namely, imbalance, rub-impact, oil whipping and misalignment, extract the samples＇ energy features, and construct integrated neural networks to recognize the samples＇ faults. The results show validity of the proposed method.
出处
《应用科学学报》
CAS
CSCD
北大核心
2007年第5期510-515,共6页
Journal of Applied Sciences
基金
国家自然科学基金资助项目(50705042)
关键词
转子
小波变换
特征提取
神经网络
故障诊断
rotor
wavelet transform
feature extracting
neural networks
fault diagnosis
