噪声与振动控制Issue(3)：170-175,6.DOI:10.3969/j.issn.1006-1335.2014.03.036
基于EEMD能量熵及LS-SVM滚动轴承故障诊断
Fault Diagnosis Method of Rolling Bearings Based on Ensemble Empirical Mode Decomposition Energy Entropy and LS-SVM
摘要
Abstract
In view of the non-stationary features of vibration signals of rolling bearings and the difficulty to collect a large number of fault samples in practice, a fault diagnosis scheme based on ensemble empirical mode decomposition (EEMD) energy entropy and least square support vector machine (LS-SVM) is proposed. First of all, original acceleration vibration signals are decomposed into several intrinsic mode functions (IMFs). Since the energy of vibration signal changes in different frequency bands when fault occurs, the fault pattern and condition can be identified by calculating and analyzing the EEMD energy entropy. The energy features extracted from several IMFs, which contain the most dominant fault information, serve as the input vector of the support vector machine to judge the technical condition of the bearing and seriousness of the fault. The diagnosis results are analyzed and compared with different kernel functions. The experimental results show that the proposed method is effective.关键词
振动与波/集合经验模态分解/固有模态函数/能量熵/最小二乘支持向量机/故障诊断Key words
vibration and wave/ensemble empirical mode decomposition/intrinsic mode function/energy entropy/LS-SVM/fault diagnosis分类
信息技术与安全科学引用本文复制引用
夏均忠,苏涛,张阳,王龙,冷永刚..基于EEMD能量熵及LS-SVM滚动轴承故障诊断[J].噪声与振动控制,2014,(3):170-175,6.基金项目
国家自然科学基金项目 （）
