噪声与振动控制Issue(6)：174-177,4.DOI:10.3969/j.issn.1006-1335.2014.06.039
基于LMD样本熵与SVM的往复压缩机故障诊断方法
Fault Diagnosis Method Based on LMD Sample Entropy and SVM for Reciprocating Compressors
摘要
Abstract
Due to the non-stationary and nonlinearity characteristics of vibration signal of reciprocating compressors, a fault diagnosis method for bearing fault of reciprocating compressor based on LMD sample entropy and SVM is proposed. To improve the envelope approximation accuracy of local mean and envelope estimation, a cubic Hermite interpolation method, which has excellent conformal characteristic, is used to construct the envelope curves for the extreme points. Vibration signals in each state are decomposed into a series of PF components with the improved LMD method, and the PF components, which contain the main information of the fault state, are chosen according to the correlation coefficient. Sample entropy of the selected PF components is calculated as eigenvectors. Taking SVM as pattern classifier, the type of bearing clearance fault is diagnosed, and the advantage of this method is proved by comparing the eigenvectors extracted by LMD with those by the approximate entropy method.关键词
振动与波/往复压缩机/LMD/样本熵/轴承/故障诊断Key words
vibration and wave/reciprocating compressor/LMD/sample entropy/bearing/fault diagnosis分类
机械制造引用本文复制引用
邹龙庆,陈桂娟,邢俊杰,姜楚豪..基于LMD样本熵与SVM的往复压缩机故障诊断方法[J].噪声与振动控制,2014,(6):174-177,4.基金项目
国家科技支撑计划项目（2012BAH28F03）；黑龙江省教育厅科学技术研究重点项目（12521051）；黑龙江省自然基金项目 （）
