作者：蔡波¹, 黄晋英¹, 杜金波², 马健程³, 王智超³

作者单位：1. 中北大学机械工程学院，山西 太原 030051;
2. 北京北方车辆集团有限公司，北京 100072;
3. 中北大学大数据学院，山西 太原 030051

关键词：行星齿轮箱;改进的集成经验模态分解;多特征融合;最小二乘支持向量机;故障诊断

摘要：

针对行星齿轮箱振动信号非线性、非平稳性特点及故障特征难以有效提取的问题，提出基于改进的集成经验模态分解(MEEMD)多特征融合和最小二乘支持向量机（LS-SVM）的行星齿轮箱故障诊断方法。首先，利用MEEMD分解不同工况下的齿轮振动信号，得到一系列固有模态分量。其次，根据相关系数筛选出3阶敏感模态分量并计算对应的样本熵和能量，将二者融合组成高维特征向量，最后，将融合特征向量作为最小支持向量机（LS-SVM）的输入，对齿轮进行故障分类。在行星齿轮箱实验台上开展实验，与基于单特征构成的特征向量进行对比，并与概率神经网络（PNN）分类算法进行对比，结果验证该方法的有效性和优越性。

Planetary gearbox fault diagnosis based on MEEMD multi-feature fusion and LS-SVM
CAI Bo¹, HUANG Jinying¹, DU Jinbo², MA Jiancheng³, WANG Zhichao³
1. School of Mechanical Engineering, North University of China, Taiyuan 030051, China;
2. Beijing North Vehicle Group Corporation, Beijing 100072, China;
3. School of Data Science and Technology, North University of China, Taiyuan 030051, China
Abstract: In order to solve the problem that the vibration signal of the planetary gearbox is nonlinear, non-stationary and difficult to be extracted effectively, a planetary gearbox fault diagnosis method based on modified ensemble empirical mode decomposition (MEEMD) multi-feature fusion and least square support vector machine (LS-SVM) is proposed. Firstly, with the MEEMD method, gear vibration signals under different working conditions are decomposed into a series of intrinsic mode functions (IMF). Secondly, the third-order sensitive modal components are screened out according to the correlation coefficient and the corresponding sample entropy and energy are calculated. The two are fused to form a high-dimensional eigenvector. Lastly, the fused eigenvector is taken as the input of the minimum support vector machine (LSSVM) to classify gear faults. Experiments are conducted on the planetary gearbox test bed, and compared with the feature vector based on single feature composition, and with the probabilistic neural network (PNN) classification algorithm. The results verify the effectiveness and superiority of the proposed method.
Keywords: planetary gearbox;MEEMD;multi-feature fusion;LS-SVM;fault diagnosis
2021, 47(9):126-132  收稿日期: 2020-06-13;收到修改稿日期: 2020-07-24
基金项目: 山西省重点研发计划（国际科技合作方面）（201903D421008）；山西省自然科学基金项目（201901D111157）
作者简介: 蔡波(1994-),男,山西临汾市人,硕士研究生,专业方向为信号处理与故障诊断
参考文献
[1] HE Z Y, SHAO H D, CHENG J S, et al. Kernel flexible and displaceable convex hull based tensor machine for gearbox fault intelligent diagnosis with multi-source signals[J]. Measurement, 2020, 163: 1-10
[2] 陈春俊, 张振, 刘广. 轨道不平顺激扰下机车传动齿轮振动特性研究[J]. 中国测试, 2020, 46(6): 108-115
[3] 但长林, 李三雁, 张彬. 基于样本熵和SVM的滚动轴承故障诊断方法研究[J]. 中国测试, 2020, 46(11): 37-42
[4] 付大鹏, 翟勇, 于青民. 基于EMD和支持向量机的滚动轴承故障诊断研究[J]. 机床与液压, 2017, 45(11): 184-187
[5] 颜丙生, 聂士杰, 汤宝平, 等. 基于阶次分析和EWT的轴承故障诊断研究[J]. 组合机床与自动化加工技术, 2018(7): 51-54
[6] 徐乐, 于如信, 邢邦圣, 等. 基于LMD能量熵的滚动轴承故障特征提取[J]. 机械传动, 2019, 43(1): 136-139
[7] WU Z H, HUANG N E. Ensemble empirical mode decomposition: a noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis, 2009, 1(1): 1-41
[8] 郑旭, 郝志勇, 金阳, 等. 基于MEEMD的内燃机辐射噪声贡献[J]. 浙江大学学报(工学版), 2012, 46(5): 954-960
[9] 王晋瑞, 谢丽蓉, 王忠强, 等. 基于MEEMD-DHENN的滚动轴承故障诊断[J]. 机械传动, 2018, 42(3): 139-143
[10] 杨超, 赵荣珍, 孙泽金. 基于SVD-MEEMD与Teager能量谱的滚动轴承微弱故障特征提取[J]. 噪声与振动控制, 2020, 40(4): 92-97
[11] 张龙, 宋成洋, 邹友军, 等. 基于VMD多特征融合与PSO-SVM的滚动轴承故障诊断[J]. 机械设计与研究, 2019, 35(6): 96-104
[12] PANDARAKONE S E, MIZUNO Y, NAKAMURA H. Distinct fault analysis of induction motor bearing usingfrequency spectrum determination and Support Vector Machine[J]. IEEE Transactions on Industry Applications, 2017, 53(3): 3049-3056
[13] 庄城城, 易辉, 张杰. EEMD多尺度熵和LSSVM在模拟电路故障诊断中的应用[J]. 微电子学与计算机, 2019, 36(10): 78-82
[14] 郭辉, 伍川辉, 刘泽潮, 等. 基于峭度与互相关的IEWT轴承故障诊断方法研究[J]. 铁道科学与工程学报, 2019, 16(7): 1774-1780
[15] CHEN J Y, ZHOU D, LYU C, et al. An integrated method based on CEEMD-SampEn and the correlation analysis algorithm for the fault diagnosis of a gearbox under different working conditions[J]. Mechanical Systems and Signal Processing, 2017, 113: 102-111
[16] PINCUS S M. Assessing serial irregularity and its implications for health[J]. Annals of the New York Academy of Sciences, 2001, 954(1): 245-267
[17] 隋文涛, 路长厚, WANG W, 等. 基于模拟退火与LSSVM的轴承故障诊断[J]. 振动. 测试与诊断, 2010, 30(2): 119-122+206