联合收割机行走变速箱故障特征提取方法研究  

Research on the Method of Extracting the Fault Features of Walking Gearbox of Combine Harvester

作　　者：吴又新 王新忠[1,2] 承银辉 王宝龙 Wu Youxin;Wang Xinzhong;Cheng Yinhui;Wang Baolong(School of Agricultural Engineering,Jiangsu University,Zhenjiang 212013,China;High-tech Key Laboratory of Modern Agricultural Equipment and Intelligentization of Jiangsu Province,Zhenjiang 212013,China;Lovol Heavy Industry Co.Ltd.,Weifang 261000,China)

机构地区：[1]江苏大学农业工程学院,江苏镇江212013 [2]江苏省农业装备与智能化高技术研究重点实验室,江苏镇江212013 [3]雷沃重工股份有限公司,山东潍坊261000

出　　处：《农机化研究》2022年第2期23-27,共5页Journal of Agricultural Mechanization Research

基　　金：“十三五”国家重点研发计划项目(2017YFD0700203)。

摘　　要：针对联合收割机行走齿轮箱故障诊断率低的问题,提出了基于遗传算法(Genetic Algorithm,GA)及样本熵优化VMD参数的故障特征提取方法,研究了不同分解算法对故障诊断率的影响,并在试验台上采集行走变速箱不同故障状态下的振动信号开展试验研究和验证。试验结果表明:与EMD样本熵和无样本熵情况相比,VMD样本熵具有维度低、识别精度高的优点,同WOA-KELM模型组合在故障诊断中有良好的识别分类性,可以用于联合收割机行走变速箱的故障诊断。Aiming at the problem of low fault diagnosis rate of the walking gear box of the combine harvester,a fault feature extraction method based on genetic algorithm(GA)and sample entropy optimization VMD parameters is proposed,and the influence of different decomposition algorithms on the fault diagnosis rate is studied The vibration signal of the walking gearbox under different fault conditions is collected on the test bench to carry out experimental research and verification.The test results show that compared with the EMD sample entropy and no sample entropy,the VMD sample entropy has the advantages of low dimensions and high recognition accuracy.The combination with the WOA-KELM model has good recognition and classification in fault diagnosis and can be used for joint Fault diagnosis of the walking gearbox of the harvester.

关 键 词：联合收割机 行走变速箱 遗传算法 变分模态分解 样本熵 

分 类 号：S225.3[农业科学—农业机械化工程] TP206.3[农业科学—农业工程]