摘要
为了解决传统小波或小波包变换方法对柴油机振动信号频率分辨率不高、易受邻近谐波分量间交叠影响的缺陷,提出了一种基于经验模态分解和支持向量机的故障诊断方法。该方法首先对振动信号进行经验模态分解,分别提取能量最大的几个基本模式分量的小波包特征;然后采用支持向量机在每个独立的特征子集中进行训练,并按该子集对应的基本模式分量的能量权重进行加权融合。试验中将该方法应用于6135型柴油机的故障诊断,结果表明,针对每个基本模式分量分别进行故障分析是可行的,能够对6135型柴油机常见故障模式进行准确识别。
To overcome the limitations of low frequency resolution and interference of aliasing distortion of neighbouring harmonic in the vibration signal analysis of diesel engine using the wavelet transform(WT)or the wavelet packet transform(WPT),a novel fault diagnosis method based on the empirical mode decomposition(EMD)and the support vector machine(SVM)is proposed.The method employs EMD to decompose the vibration signal into many intrinsic mode functions(IMFs),and extracts the wavelet packet features of several energy-dominating IMFs respectively.Then,the SVM-based classifiers are trained in each of feature sub-spaces and fused by the energy weight of each IMF.The proposed method is applied to fault diagnosis of a 6135-type diesel engine,the results show that the independent IMF analysis for fault diagnosis is feasible,and the proposed method can effectively diagnose the conventional faults of 6135-type diesel engines.
出处
《振动．测试与诊断》
EI
CSCD
北大核心
2010年第1期19-22,共4页
Journal of Vibration,Measurement & Diagnosis
基金
国家自然科学基金资助项目(编号:60443006)
关键词
故障诊断
经验模态分解
基本模式分量
支持向量机
小波包变换
fault diagnosis empirical mode decomposition intrinsic mode function support vector machine wavelet packet transform
