首页|基于JS-VME-DBN和MS-UMAP的行星齿轮箱故障诊断方法

基于JS-VME-DBN和MS-UMAP的行星齿轮箱故障诊断方法

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为了解决行星齿轮箱振动信号存在噪声干扰和特征提取困难的问题,提出一种基于水母搜索优化变分模态提取(JS-VME)、深度置信网络(DBN)和监督型马氏距离的均匀流形逼近与投影算法(MS-UMAP)的行星齿轮箱故障诊断方法.采集行星齿轮箱的振动信号,利用JS-VME对其进行预处理,获得相关性较强的期望IMF(intrinsic mode function)分量;然后将该IMF分量应用DBN提取特征向量,构建高维故障特征集;采用MS-UMAP进行维数约减,获得低维、敏感的故障特征;将低维故障特征集应用水母搜索优化核极限学习机(JS-KELM)判别故障类型.行星齿轮箱故障诊断实验结果表明:与UMAP、t-SNE、Isomap、LPP、W-Isomap、LLE、LTSA和MDS等方法相比,MS-UMAP算法对JS-VME-DBN的特征提取结果有着最佳的降维效果,所提方法对行星齿轮箱的裂纹、磨损和缺齿等故障的识别率达到了 100％,具有一定的有效性.
Fault diagnosis method of planetary gearbox based on JS-VME-DBN and MS-UMAP
In order to solve the problem of the noise interference and the difficulty in feature extraction in the vibration signal of planetary gearbox,a fault diagnosis method for planetary gearboxes based on jel-lyfish search optimization variational mode extraction(JS-VME),deep belief network(DBN)and supervised Mahalanobis distance uniform manifold approximation and projection algorithms(MS-UMAP)was proposed.The vibration signals of the planetary gearbox were collected,and JS-VME was used to preprocess them to obtain expected IMF(intrinsic mode function)component with strong correlation.Then,DBN was applied to the IMF component to extract feature vectors,and the high-dimensional fault feature set was built.MS-UMAP was used for dimensionality reduc-tion to obtain low-dimensional and sensitive fault features.The low-dimensional fault feature set was applied to the jellyfish search optimization kernel extreme learning machine(JS-KELM)to de-termine fault types.The experiment results of planetary gearbox fault diagnosis showed that com-pared with UMAP,t-SNE,Isomap,LPP,W-Isomap,LLE,LTSA and MDS,the MS-UMAP algorithm had the best dimensionality reduction effect on the feature extraction results of JS-VME-DBN.The fault recognition rate of the proposed method reached 100％with a certain validity in planetary gearbox,such as the cracks,wear and missing teeth.

fault diagnosisplanetary gearboxvariational mode extraction(VME)deep belief network(DBN)uniform manifold approximation and projection(UMAP)kernel extreme learning machine(KELM)

戚晓利、程主梓、崔创创、杨艳

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安徽工业大学机械工程学院,安徽马鞍山 243032

故障诊断 行星齿轮箱 变分模态提取(VME) 深度置信网络(DBN) 均匀流行逼近与投影算法(UMAP) 核极限学习机(KELM)

安徽省自然科学基金

1808085ME152

2024

10.13224/j.cnki.jasp.20220221

航空动力学报
中国航空学会

航空动力学报

CSTPCD北大核心
影响因子：0.59
ISSN：1000-8055
年,卷(期)：2024.39(3)
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