| 类间数据不均衡条件下基于平衡随机森林的轴向柱塞泵故障诊断方法 |
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| 引用本文： | 姜万录,马歆宇,岳毅,赵亚鹏. 类间数据不均衡条件下基于平衡随机森林的轴向柱塞泵故障诊断方法[J]. 液压与气动, 2022, 0(3): 45-54. DOI: 10.11832/j.issn.1000-4858.2022.03.006 |
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| 作者姓名： | 姜万录 马歆宇 岳毅 赵亚鹏 |
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| 作者单位： | 1.燕山大学河北省重型机械流体动力传输与控制重点实验室, 河北秦皇岛 066004; 2.燕山大学先进锻压成形技术与科学教育部重点实验室, 河北秦皇岛 066004 |
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| 基金项目： | 国家自然科学基金(51875498)；;河北省自然科学基金重点项目(E2018203339)； |
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| 摘 要： | 针对轴向柱塞泵实际故障诊断中采集到的故障类数据远少于正常类数据的情况,为提升故障分类精确率,提出了一种基于平衡随机森林(Balanced Random Forest,BRF)的轴向柱塞泵故障诊断方法.BRF算法是随机森林(Random Forest,RF)的改进算法,将欠采样方法与RF结合,强化了RF处理非均衡数据的能...
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| 关 键 词： | 轴向柱塞泵 故障诊断 非均衡数据 平衡随机森林 多分类 |
| 收稿时间： | 2021-06-10 |
Fault Diagnosis Method of Axial Piston Pump Based on Balanced Random Forest Under Imbalanced Datasets |
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| JIANG Wan-lu,MA Xin-yu,YUE Yi,ZHAO Ya-peng. Fault Diagnosis Method of Axial Piston Pump Based on Balanced Random Forest Under Imbalanced Datasets[J]. Chinese Hydraulics & Pneumatics, 2022, 0(3): 45-54. DOI: 10.11832/j.issn.1000-4858.2022.03.006 |
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| Authors: | JIANG Wan-lu MA Xin-yu YUE Yi ZHAO Ya-peng |
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| Affiliation: | 1. Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao, Hebei066004; 2. Key Laboratory of Advanced Forging & Stamping Technology and Science, Ministry of Education, Yanshan University, Qinhuangdao, Hebei066004 |
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| Abstract: | Aiming at the situation that fault data collected in actual fault diagnosis of axial piston pump is far less than normal data, an axial piston pump fault diagnosis method based on Balanced Random Forest (BRF) algorithm is proposed to improve the classification precision of faulty classes. BRF algorithm is an improved algorithm of Random Forest (RF) which combines under-sampling method with RF and strengthens the ability of RF to process imbalanced data. The algorithm performance is tested on the UCI open source datasets. Compared with RF and SMOTE-RF, BRF algorithm improves the precision of minority classes. Finally, BRF algorithm is applied to the axial piston pump fault diagnosis. The results show that BRF has higher classification precision of faulty classes than RF and SMOTE-RF under imbalanced datasets. |
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| Keywords: | piston pump fault diagnosis imbalanced data Balanced Random Forest (BRF) multi-class classification |
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