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变分模态分解与极限梯度提升树融合的高速轴向柱塞泵空化等级识别
引用本文:王立尧,王远航,孟苓辉,李小兵,潮群,陶建峰,刘成良. 变分模态分解与极限梯度提升树融合的高速轴向柱塞泵空化等级识别[J]. 液压与气动, 2021, 0(5): 62-67. DOI: 10.11832/j.issn.1000-4858.2021.05.009
作者姓名:王立尧  王远航  孟苓辉  李小兵  潮群  陶建峰  刘成良
作者单位:1.上海交通大学机械系统与振动国家重点实验室, 上海 200240;2.工业和信息化部电子第五研究所, 广东广州 510610;3.广东省电子信息产品可靠性技术重点实验室, 广东广州 510610
基金项目:中国博士后科学基金(2019M660086)
摘    要:针对高速轴向柱塞泵在不同空化程度下故障特征不明显导致识别准确率低的问题,提出了一种变分模态分解和极限梯度提升树融合的识别方法.在不同空化等级下进行高速轴向柱塞泵空化试验,采集壳体的振动加速度信号,对信号采用变分模态分解方法并从中提取故障特征以构造特征数据集,最后利用极限梯度提升树进行空化等级的识别.为证明所提方法的抗噪...

关 键 词:高速轴向柱塞泵  空化等级识别  变分模态分解  极限梯度提升树
收稿时间:2019-12-02

Identification of Cavitation Intensity of High-speed Axial Piston Pumps Based on Variational Mode Decomposition and XGBoost
WANG Li-yao,WANG Yuan-hang,MENG Ling-hui,LI Xiao-bing,CHAO Qun,TAO Jian-feng,LIU Cheng-liang. Identification of Cavitation Intensity of High-speed Axial Piston Pumps Based on Variational Mode Decomposition and XGBoost[J]. Chinese Hydraulics & Pneumatics, 2021, 0(5): 62-67. DOI: 10.11832/j.issn.1000-4858.2021.05.009
Authors:WANG Li-yao  WANG Yuan-hang  MENG Ling-hui  LI Xiao-bing  CHAO Qun  TAO Jian-feng  LIU Cheng-liang
Affiliation:1. State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240;2. China Electronic Product Reliability and Environmental Testing Research Institute, Guangzhou, Guangdong 510610;3. Guangdong Provincial Key Laboratory of Electronic Information Products Reliability Technology, Guangzhou, Guangdong 510610
Abstract:Aiming at the problems of implicit fault features and low identification accuracy of high-speed axial piston pumps, a recognition method based on Variational Mode Decomposition (VMD) and XGBoost is proposed in this paper. Cavitation experiments are carried out to collect the vibration signals on the pump housing under different levels of cavitation intensity. The vibration signals are decomposed by variational mode to obtain fault features and XGBoost model is used to identify cavitation intensity levels for high-speed axial piston pumps. In addition, random Gaussian white noise is added to the test dataset to prove the anti-noise performance of the proposed method. The results show that the proposed model can still accurately identify cavitation conditions on the noisy signals with different signal-to-noise ratios.
Keywords:high-speed axial piston pump  cavitation intensity identification  variational mode decomposition  XGBoost  
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