基于双频精细复合多尺度排列熵的齿轮箱损伤识别  

Damage identification of gearbox based on dual-frequency refined composite multiscale permutation entropy

作　　者：刘心 费莹 李倩[4] LIU Xin;FEI Ying;LI Qian(Institute of Information Technology,Guilin University of Electronic Technology,Guilin 514004,China;College of Mechanical&Electrical Engineering,Sanmenxia Vocational College of Social Management,Sanmenxia 472000,China;Jintanglang Institute of Architecture,Soochow University,Suzhou 215123,China;Department of Electronic Engineering,Zhejiang Automotive Vocational and Technical College,Linhai 317000,China)

机构地区：[1]桂林电子科技大学信息科技学院,广西桂林541004 [2]三门峡社会管理职业学院机电工程学院,河南三门峡472000 [3]苏州大学金螳螂建筑学院,江苏苏州215123 [4]浙江汽车职业技术学院电子工程系,浙江临海317000

出　　处：《机电工程》2023年第8期1176-1184,共9页Journal of Mechanical & Electrical Engineering

基　　金：浙江省教育科学规划课题(2021SCG303);苏州大学虚拟仿真实验教学重点培育项目(2019-04);虚拟仿真实验教学创新联盟2021年度第二批实验教学优质创新课程培育项目(202102XF03)。

摘　　要：齿轮箱振动信号的非线性会导致其损伤特征难以得到有效提取,针对这一问题,提出了一种基于双频精细复合多尺度排列熵(DFRCMPE)和鲸鱼算法优化支持向量机(WOA-SVM)的融合损伤识别方法。首先,采用小波包分解(WPD)对齿轮箱损伤振动信号进行了两层分解,获得了反映齿轮箱损伤特性的低频和高频分量;然后,利用精细复合多尺度排列熵(RCMPE)对两组频带分量进行了分析,以充分提取嵌入在振动信号中的损伤信息,构建损伤特征;最后,将损伤特征输入至WOA-SVM分类模型中,成功对损伤进行了智能识别,并以实验采集到的齿轮箱振动信号为对象,对基于DFRCMPE和WOA-SVM的融合损伤识别方法的有效性开展了对比讨论。研究结果表明:与基于精细复合多尺度样本熵(RCMSE)、精细复合多尺度模糊熵(RCMFE)、RCMPE、精细复合多尺度散布熵(RCMDE)的特征提取方法相比,基于DFRCMPE和WOA-SVM的融合损伤识别方法的准确率和稳定性更高,平均识别准确率达到了100%;该方法能够为解决实际应用中的齿轮箱故障识别问题提供可行的思路。Aiming at the problem that the nonlinearity of gearbox vibration signal made it difficult to effectively extract the damage feature,a fusion damage identification method based on dual-frequency refined composite multi-scale permutation entropy(DFRCMPE)and whale algorithm optimized support vector machine(WOA-SVM)was developed.First,the wavelet packet decomposition(WPD)was used to decompose the gearbox damage vibration signal in two layers to obtain the low-frequency and high-frequency components reflecting the gearbox damage characteristics.Then,the refined composite multi-scale permutation entropy(RCMPE)was used to analyze the two groups of frequency band components to fully extract the damage information embedded in the vibration signal,and constructed the damage characteristics.Finally,the damage features were input into the whale algorithm optimization support vector machine classification model to achieve intelligent damage identification;and the effectiveness of the fusion damage identification method based on DFRCMPE and WOA-SVM was discussed by taking the gearbox vibration signal collected in the experiment as the research object.The research results show that comparing with the feature extraction methods based on refined composite multi-scale entropy(RCMSE),refined composite multi-scale fuzzy entropy(RCMFE),RCMPE and refined composite multi-scale dispersion entropy(RCMDE),the fusion damage identification method based on DFRCMPE and WOA-SVM has higher accuracy and stability,and the average recognition accuracy reaches 100%;this method can provide a feasible way to solve the problem of gearbox fault identification in practical applications.

关 键 词：齿轮传动 损伤特征提取 齿轮箱振动信号 双频精细复合多尺度排列熵 鲸鱼算法优化支持向量机 小波包分解 

分 类 号：TH132.41[机械工程—机械制造及自动化]