基于角域重采样与VMD的电梯曳引轮轴承故障诊断方法  

Fault diagnosis method of elevator traction wheel bearing based on angular domain resampling and VMD

作　　者：魏义敏 刘辉[1] 杨乐红 WEI Yi-min;LIU Hui;YANG Le-hong(Zhejiang Key Laboratory of Mechanical and Electrical Product Reliability Technology,Zhejiang Sci-Tech University,Hangzhou 310018,China)

机构地区：[1]浙江理工大学浙江省机电产品可靠性技术研究重点实验室,浙江杭州310018

出　　处：《机电工程》2023年第8期1259-1266,共8页Journal of Mechanical & Electrical Engineering

基　　金：国家自然科学基金面上项目(52075496,51505430)。

摘　　要：针对曳引轮轴承故障诊断效果易受变转速工况和环境噪声影响的问题,提出了一种基于轿厢运行速度估计曳引轮转速的角域重采样方法,以及基于麻雀搜索算法(SSA)优化变分模态分解(VMD)参数的曳引轮轴承故障诊断方法。首先,采用轿厢运行速度估计曳引轮转速的方法,对曳引轮轴承振动信号进行了角域重采样;然后,采用SSA算法优化VMD参数的方法,对振动信号进行了分解,并根据最大峭度准则选取了分量,提取了曳引轮轴承故障特征指标;最后,搭建了电梯试验台,开展了故障注入试验,构建了多种分类模型,对基于角域重采样与SSA-VMD的曳引轮轴承故障诊断方法的有效性进行了验证。研究结果表明:曳引轮轴承角域重采样后的诊断效果明显优于未角域重采样的诊断效果,故障识别率提高了5%以上;实验条件下采用SSA-VMD方法能够准确地提取曳引轮轴承故障特征,故障识别率可达到95%。Aiming at the problem that the fault diagnosis effect of traction wheel bearing was easily affected by variable speed working conditions and environmental noise,a fault diagnosis method of traction wheel bearing based on angle resampling method for estimating traction wheel speed based on car running acceleration,and a fault diagnosis method of traction wheel bearing based on sparrow search algorithm for optimizing variational mode decomposition parameters were proposed.Firstly,the method of estimating the speed of the traction pulley was used to realize the angular resampling of the vibration signal of the traction wheel bearing.Then,the sparrow search algorithm(SSA)was used to optimize the variational mode decomposition(VMD)parameters,the vibration signal was decomposed and the components were selected according to the maximum kurtosis criterion,and the fault characteristics of the traction wheel bearing were extracted.Finally,the elevator test bench was built to carry out the fault injection test,and a variety of classification models were constructed to verify the fault diagnosis method of traction wheel bearing based on angle resampling and the method of SSA-VMD.The research results show that,the diagnostic effect of angular resampling is obviously better than that of non-angular resampling,and the fault recognition rate is increased by more than 5%.The SSA-VMD method can accurately extract the fault features of the traction wheel bearing under the experimental conditions,and the fault recognition rate reaches 95%.

关 键 词：曳引轮轴承 故障诊断 角域重采样 变分模态分解 麻雀搜索算法 

分 类 号：TH17[机械工程—机械制造及自动化] TU857[建筑科学]