【摘要】 提出一种新的轴承故障特征提取方法——层次模糊熵(Hierarchical Fuzzy Entropy,HFE)。层次模糊熵包括层次分析和模糊熵计算。与多尺度模糊熵相比,层次模糊熵既分析信号的低频分量又分析信号的高频分量,因而能提取更全面、准确的故障信息。改进支持向量机(Improved support vector machine based binary tree,ISVMBT)相比其他多分类器具有识别率更高的优势,因此提出了一种基于层次模糊熵和改进支持向量机的轴承故障诊断方法。首先将HFE作为故障特征提取工具,然后将所得的特征向量输入到改进支持向量机进行模式识别。通过轴承故障诊断的工程应用,表明该方法可以有效提取轴承故障特征,实现轴承不同故障类型和故障程度的准确识别。更多还原

【Abstract】 A new rolling bearing fault feature extractor called hierarchical fuzzy entropy(HFE)is proposed in this paper,which is composedcomprises the of hierarchical procedure and the fuzzy entropy calculation.Compared with multi-scale fuzzy entropy(MFE)method,HFE method considers both the low and high frequency components of the vibration signals,which can provide a much more accurate estimation of entropy.Besides,improved support vector machine based binary tree SVM(ISVMBT)has the priority of high recognition accuracy compared with other classifiers.HenceTherefore,in this paper we proposed a novel rolling bearing fault diagnosis method based on HFE and ISVM-BT is proposed in this paper.Firstly,HFE is utilized to extract fault features and then the fault features are fed into the ISVM-BT to automatically fulfill the fault patterns identifications.The experimental results show the proposed method is effective in recognizing the different categories and severities of rolling bearings.更多还原