| 周靖南,唐宏宾,任广安,梁翊骁.机理模型与数据驱动融合的液压泵变载荷工况故障诊断方法[J].电子测量与仪器学报,2025,39(4):247-257 |
| 机理模型与数据驱动融合的液压泵变载荷工况故障诊断方法 |
| Fault diagnosis method for hydraulic pumps under variable load conditionsbased on the fusion of mechanistic models and data-driven approaches |
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| DOI： |
| 中文关键词: 液压泵 故障诊断 机理模型 数据驱动 |
| 英文关键词:hydraulic pumps fault diagnosis mechanistic model data-driven approaches |
| 基金项目:国家重点研发计划项目（2023YFB3406104）、湖南省教育厅重点项目（22A0222）、湖南省自然科学基金（2022JJ40550）、长沙理工大学研究生科研创新项目（CSLGCX23042）资助 |
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| Author | Institution |
| Zhou Jingnan | School of Automobile and Mechanical Engineering, Changsha University of Science & Technology, Changsha 410004, China |
| Tang Hongbin | School of Automobile and Mechanical Engineering, Changsha University of Science & Technology, Changsha 410004, China |
| Ren Guangan | School of Automobile and Mechanical Engineering, Changsha University of Science & Technology, Changsha 410004, China |
| Liang Yixiao | School of Automobile and Mechanical Engineering, Changsha University of Science & Technology, Changsha 410004, China |
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| 摘要点击次数: 39 |
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| 中文摘要: |
| 由于工作环境恶劣、工况复杂多变，液压泵常处于变载荷的工作状态，给其状态监测及故障诊断带来了严峻挑战。然而现有的基于模型的方法和数据驱动方法在故障诊断上均有一定的局限性，故提出了一种机理模型与数据驱动融合的故障诊断方法。首先构建液压泵虚拟样机模型并模拟不同负载下的故障，获取仿真压力信号；然后对液压泵进行故障实验，采集与仿真信号相对应的负载和故障状态的实验压力信号；随后，根据提出的方差权值融合方法计算仿真和实验数据方差，将通过方差计算出的最优权值对仿真和实验数据进行融合；最后将获得的融合数据输入首层宽卷积深度神经网络(WDCNN)进行单一负载和混合负载两种情况下的故障诊断。实验结果表明，该方法能明显提高诊断的准确率，其中在混合负载情况下该方法比单一的模型驱动和数据驱动诊断方法准确率分别提高2.42%和12.92%，验证了该方法的有效性与优越性。 |
| 英文摘要: |
| Due to the harsh working environment and complex working conditions, the hydraulic pump is often in the working state of variable load, severely challenging its condition monitoring and fault diagnosis. However, the existing model-based and data-driven methods have some limitations in fault diagnosis, so a fault diagnosis method based on the fusion of mechanism model and data-driven is proposed. First, the virtual prototype model of the hydraulic pump is built, and the faults under different loads are simulated to obtain the simulation pressure signal. Then, the hydraulic pump is tested for fault, and the experimental pressure signals of load and fault state corresponding to the simulation signal are collected. Following that, the variance of simulation and experimental data is calculated according to the proposed variance weight fusion method, and the optimal weight calculated by variance is used to fuse the simulation and experimental data. Finally, the fusion data is input into the deep convolutional neural networks with wide first-layer kernels for fault diagnosis under single and mixed loads. Experimental results show that this method can significantly improve the accuracy of diagnosis, and the accuracy is 2.42% and 12.92% higher than that of single model-driven and data-driven diagnosis methods in the case of mixed load, which verifies the effectiveness and superiority of this method. |
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