| 陈保家,陈学力,肖文荣,陈法法,肖能齐,刘 强.小样本下滚动轴承故障的多源域迁移诊断方法[J].电子测量与仪器学报,2022,36(2):219-228 |
| 小样本下滚动轴承故障的多源域迁移诊断方法 |
| Multi-source domain transfer diagnosis method forrolling bearing faults under small samples |
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| DOI： |
| 中文关键词: 多源域迁移学习 卷积神经网络 滚动轴承 故障诊断 |
| 英文关键词:multi-source domain transfer learning convolution neural network rolling bearing fault diagnosis |
| 基金项目:国家自然科学基金（51975324）、机械传动国家重点实验室开放基金 (SKLMT MSKFKT 202020)、湖北省重点实验室开放基金（2020KJX02，2020KJX05）项目资助 |
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| Author | Institution |
| Chen Baojia | 1. Hubei Key Laboratory of Hydroelectric Machinery Design & Maintenance, China Three Gorges University,2. The State Key Laboratory of Mechanical Transmission, Chongqing University |
| Chen Xueli | 3. College of Mechanical and Power Engineering, China Three Gorges University |
| Xiao Wenrong | 1. Hubei Key Laboratory of Hydroelectric Machinery Design & Maintenance, China Three Gorges University |
| Chen Fafa | 1. Hubei Key Laboratory of Hydroelectric Machinery Design & Maintenance, China Three Gorges University,2. The State Key Laboratory of Mechanical Transmission, Chongqing University |
| Xiao Nengqi | 1. Hubei Key Laboratory of Hydroelectric Machinery Design & Maintenance, China Three Gorges University |
| Liu Qiang | 1. Hubei Key Laboratory of Hydroelectric Machinery Design & Maintenance, China Three Gorges University |
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| 中文摘要: |
| 为了减小神经网络在机械设备故障预示与健康管理(PHM)过程中对大量完备数据的依赖,针对数据稀少情况下的滚
动轴承故障诊断问题,提出了一种多源域迁移学习方法。 模型采用一维卷积神经网络(1D-CNN),以原始振动信号作为模型的
输入,利用两个不同的源域数据依次对模型进行预训练,使用目标域数据对预训练模型进行微调,提高对目标域的识别精度。
采用频询实验台实测数据及西储大学数据集,在目标域故障样本不足的情况下分别对模型的分类精度、训练速度、结果稳定性、
多源域有效性进行验证,并与卷积神经网络(CNN)、迁移成分分析(TCA)、联合分布适配(JDA)、支持向量机(SVM)的诊断结果
进行对比。 实验结果表明,在故障数据稀少时,模型能达到较高的分类精度,在目标域样本数量不同的 3 种情况下,多源域迁移
方法分类精度分别达到了 97. 71%、96. 28%、94. 18%,并且模型有着较快的收敛速度,较好的稳定性。 |
| 英文摘要: |
| In order to reduce the dependence of neural network on a large number of complete data in the process of mechanical
equipment fault prediction and health management (PHM). To solve the problem of rolling bearing fault diagnosis with scarce data, a
multi-source domain transfer learning method is proposed. The model uses one-dimensional convolution neural network, takes the original
vibration signal as the input of the model, uses two different source domains data to pre-train the model, and uses the target domain data
to fine-tune the pre-training model to improve the recognition accuracy of the target domain. Using the measured data of the machinery
fault simulator of spectra quest and the bearing data sets of Case Western Reserve University, in the case of few fault samples in the
target domain, the classification accuracy, training speed, result stability and multi-source domain effectiveness of the model are verified
respectively, and the results of migration diagnosis were compared with those of CNN, TCA JDA and SVM. The results show that the
model can achieve higher classification accuracy when the fault data is scarce. In the case of the number of samples in the three target
domains, the classification accuracy of the multi-source domain migration method reaches 97. 71%, 96. 28% and 94. 18%, respectively.
The model has fast convergence speed and good stability. |
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