电子测量与仪器学报

陈剑,程明.基于 tSNE-ASC 特征选择和 DSmT 融合决策的滚动轴承声振信号故障诊断[J].电子测量与仪器学报,2022,36(4):195-204

基于 tSNE-ASC 特征选择和 DSmT 融合决策的滚动轴承声振信号故障诊断

Fault diagnosis of rolling bearing acoustic vibration signal based ontSNE-ASC feature selection and DSmT evidence fusion

DOI：

中文关键词: 声振信号轴承故障诊断变分模态分解 t 分布随机邻近嵌入平均轮廓系数 DSmT 融合决策

英文关键词:acoustic vibration signal bearing fault diagnosis variational mode decomposition t-distribution random adjacent embedding mean silhouette coefficient DSmT fusion decision

基金项目:国家自然科学基金青年基金（11604070）、安徽省科技重大专项（17030901049）项目资助

作者	单位
陈剑	1. 合肥工业大学噪声振动研究所，2. 安徽省汽车 NVH 技术研究中心
程明	1. 合肥工业大学噪声振动研究所，2. 安徽省汽车 NVH 技术研究中心

Author	Institution
Chen Jian	1. Institute of Sound and Vibration Research, Hefei University of Technology,2. Automotive NVH Engineering & Technology Research Center Anhui Province
Cheng Ming	1. Institute of Sound and Vibration Research, Hefei University of Technology,2. Automotive NVH Engineering & Technology Research Center Anhui Province

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中文摘要:

针对滚动轴承早期故障特征微弱且难以有效辨识的问题,提出一种基于 tSNE-ASC 特征选择和 DSmT 融合决策的滚动轴承声振信号故障诊断方法。利用多个传感器采集轴承在不同故障模式下的声振信号,将每个信号通过 VMD 分解得到 K 个 IMF 分量;对各个 IMF 分量进行特征提取,构建各个特征的数据集矩阵;利用 tSNE 将各特征数据集矩阵降维至二维,计算平均轮廓系数(ASC);根据 ASC 大于临界值提取出声振故障信号的敏感特征;基于诊断模型实现轴承故障的初级诊断;利用 DSmT 将声振信号初级诊断结果进行融合决策,得出最终的诊断结论。实验结果表明:基于 tSNE-ASC 的特征选择方法能有效提取混合域特征中的敏感特征,在不同工况、不同诊断模型中均具有很高的诊断精度;DSmT 决策融合有效降低了单一信号诊断的不确定性,在变载荷和升降速非平稳工况下均有很高的诊断精度。

英文摘要:

Aiming at the problem that the early fault features of rolling bearings are weak and difficult to be effectively identified, this paper proposes a fault diagnosis method for rolling bearings which is based on tSNE-ASC feature selection and DSmT fusion decision. Multiple sensors were used to collect bearing acoustic signals under different fault modes, and each signal was decomposed by VMD to obtain multiple IMF components. Feature extraction was carried out for each IMF component, and data set matrix of each feature was constructed. TSNE was used to reduce the dimension of the matrix of each feature data set to two dimensions and calculate the average contour coefficient (ASC). According to the fact that ASC greater than critical value, the sensitive features of acoustic fault signal are extracted. The primary diagnosis of bearing fault is realized based on diagnosis model. DSmT is used to fuse the primary diagnosis result of acoustic signal and get the final diagnosis conclusion. Experimental results show that the tSNE-ASC feature selection method can effectively extract sensitive features in the mixed domain, and has high diagnostic accuracy in different working conditions and different diagnostic models. DSmT decision fusion effectively reduces the uncertainty of single signal diagnosis, and has high diagnostic accuracy under the condition of variable load and non-stationary speed up and down.

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