| 马超,郑鑫辉,王少红,徐小力.基于SPWVD与知识蒸馏的行星变速器故障诊断研究[J].电子测量与仪器学报,2024,38(5):29-37 |
| 基于SPWVD与知识蒸馏的行星变速器故障诊断研究 |
| Research on fault diagnosis of planetary transmission based onSPWVD and knowledge distillation |
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| DOI： |
| 中文关键词: 故障诊断 SPWVD 知识蒸馏 MobileNet 行星变速器 |
| 英文关键词:Fault diagnosis SPWVD knowledge distillation MobileNet planetary transmission |
| 基金项目: |
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| Author | Institution |
| Ma Chao | 1.Key Laboratory of Modern Measurement and Control Technology Ministry of Education, Beijing Information Science & Technology
University, Beijing 100192, China; 2.Beijing Key Laboratory of Measurement and Control of Mechanical and Electrical System,
Beijing Information Science & Technology University, Beijing 100192, China |
| Zheng Xinhui | 1.Key Laboratory of Modern Measurement and Control Technology Ministry of Education, Beijing Information Science & Technology
University, Beijing 100192, China; 2.Beijing Key Laboratory of Measurement and Control of Mechanical and Electrical System,
Beijing Information Science & Technology University, Beijing 100192, China |
| Wang Shaohong | 1.Key Laboratory of Modern Measurement and Control Technology Ministry of Education, Beijing Information Science & Technology
University, Beijing 100192, China; 2.Beijing Key Laboratory of Measurement and Control of Mechanical and Electrical System,
Beijing Information Science & Technology University, Beijing 100192, China |
| Xu Xiaoli | 1.Key Laboratory of Modern Measurement and Control Technology Ministry of Education, Beijing Information Science & Technology
University, Beijing 100192, China; 2.Beijing Key Laboratory of Measurement and Control of Mechanical and Electrical System,
Beijing Information Science & Technology University, Beijing 100192, China |
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| 摘要点击次数: 225 |
| 全文下载次数: 10465 |
| 中文摘要: |
| 行星变速器运行工况多是非平稳运行工况，运行过程中齿轮啮合振动信号相互耦合导致测试信号混叠，其隐藏故障诊断难度增大；同时应用复杂的神经网络模型进行故障诊断预测时多数会收到工业现场边缘计算设备硬件的限制。针对相关问题，在保证行星变速器故障诊断准确率的情况下减少网络模型的参数量，提出了一种应用平滑伪维格纳-威利分布(smooth and pseudo Wigner-Vile distribution,SPWVD)与知识蒸馏结合的智能识别模型用于行星变速器故障诊断。首先，利用集合经验模态分解(EEMD)方法将多分量振动信号分解后选取单分量信号进行SPWVD计算后线性叠加得到二维时频图作为输入，以ResNet101为教师模型指导学生模型MobileNet进行训练，复杂教师模型将数据中的知识传授给学生模型，提高了学生模型的精度。将该方法与同类方法进行了对比，结果表明，模型以牺牲2.43%准确率为代价，存储成本下降为教师模型的24.55%，相较未知识蒸馏的MobileNet的准确率提高了9.61%，实现模型轻量化。本研究方法对提高深度学习模型在工程实际应用，降低边缘计算设备部署成本提供了一种有效且可行的解决方法。 |
| 英文摘要: |
| The operating conditions of planetary transmission are mostly non-stationary operating conditions. During the operation, the gear meshing vibration signals are coupled with each other, which leads to the aliasing of test signals, and the difficulty of hidden fault diagnosis increases. At the same time, when applying complex neural network models for fault diagnosis and prediction, most of them will be limited by the hardware of industrial field edge computing equipment. Aiming at the related problems, an intelligent recognition model based on smooth and pseudo Wigner-Vile distribution (SPWVD) and knowledge distillation is proposed to reduce the parameters of the network model while ensuring the accuracy of planetary transmission fault diagnosis. First, the ensemble empirical mode decomposition (EEMD) method is used to decompose the multi-component vibration signal, then the single-component signal is selected for SPWVD calculation and linearly superimposed to obtain a two-dimensional time-frequency diagram as input. The ResNet101 is used as the teacher model to guide the student model MobileNet for training. The complex teacher model imparts the knowledge in the data to the student model, which improves the accuracy of the student model.The method is compared with similar methods. The results show that the storage cost of the model is reduced to 24.55% of the teacher model at the expense of 2.43% accuracy, which is 9.61% higher than that of MobileNet without knowledge distillation. This research method provides an effective and feasible solution to improve the practical application of deep learning model in engineering and reduce the deployment cost of edge computing equipment. |
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