基于LMD与多重分形寻优的往复压缩机故障特征识别方法

摘要

Aimed at nonstationary and nonlinear characteristics of vibration signal of reciprocating compressor,generalized dimen-sions is a simple,rapid and efficient characteristic parameter extraction method in the multifractal theory. However, the sensitivity of noise for the waved local fractal dimension made the fault state separability is poor.The optimized local mean decomposition(LMD)al-gorithm was used based on wavelet denoising,in order to highlight the status of the main information by combined with the correlation coefficient to extract the main component of product function(PF). The idea of multi-scale integer optimization to generalized dimen-sions was introduced,based on calculated the maximum of average distance between the states whereby best separability, the feature matrix was constructed. Support vector machine(SVM)and incremental learning K adjacent(IKNNModel)statistical algorithm train-ing and sample recognition were explored. Comparison indicates that the proposed algorithm can improve the fault feature separability and recognition accuracy.%针对往复压缩机振动信号的非平稳和非线性特性,多重分形广义谱是一种简便、快速有效的特征参数提取方法;但其对噪声敏感,使得谱值波动,部分故障类间特征可分性差.利用经小波降噪后的优化LMD算法,并结合相关系数提取PF主分量以突出状态主信息,将多尺度整数寻优观点引入广义维,基于最佳可分性角度计算状态间最大平均距离,构造广义维数特征矩阵;通过SVM与增量学习K邻近(IKNNModel)统计算法训练和识别样本,对比证明该法能提高故障特征类间可分性和识别准确性.