| 刘馨雅,马超,黄民,张占一.变转速工况下基于角度重采样与PCA-XGBoost
轴承故障诊断方法研究[J].电子测量与仪器学报,2024,38(3):45-54 |
| 变转速工况下基于角度重采样与PCA-XGBoost
轴承故障诊断方法研究 |
| Research on angle resampling and PCA-XGBoost bearing fault diagnosismethod under variable speed working condition |
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| DOI： |
| 中文关键词: 变转速 轴承 PCA XGBoost |
| 英文关键词:bearings with variable speed conditions bearings PCA XGBoost |
| 基金项目:北京市科学技术概念验证项目（20220481077）资助 |
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| Author | Institution |
| Liu Xinya | 1.College of Electromechanical Engineering, Beijing Information Science and Technology University, Beijing 100192, China;
2.Key Laboratory of Modern Measurement and Control Technology Ministry of Education, Beijing Information Science and
Technology University, Beijing 100192, China |
| Ma Chao | Key Laboratory of Modern Measurement and Control Technology Ministry of Education, Beijing Information Science and
Technology University, Beijing 100192, China |
| Huang Min | 1.College of Electromechanical Engineering, Beijing Information Science and Technology University, Beijing 100192, China;
2.Key Laboratory of Modern Measurement and Control Technology Ministry of Education, Beijing Information Science and
Technology University, Beijing 100192, China |
| Zhang Zhanyi | China Orient Institute of Noise and Vibration, Beijing 100085, China |
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| 中文摘要: |
| 针对变转速工况下,轴承振动信号容易发生信号特征混叠、频率偏移、信号截断和噪声污染问题,提出了一种结合角度重采样、主成分分析(PCA)和极端梯度提升树(XGBoost)的故障分类模型。首先,采用脉冲信号估计轴承转速的方法对轴承振动信号进行角度重采样并求取时频特征指标;其次,利用主成分分析(PCA)对时频特征参数进行降维核心提取,选取总贡献大于95%的主元作为XGBoost模型的输入样本;最后,利用网格搜索法对XGBoost进行主要参数调优,并划分训练集和测试集对该模型进行训练,验证其故障分类的准确性。结果表明该方法的故障诊断准确率为96.44%,相较于未降维后的数据运行时间缩短了27.24 s,且角度重采样后的诊断效果明显优于未角度重采样的诊断效果,故障识别率提高了7%以上,证明所提方法能够更加快速、准确的做出诊断。 |
| 英文摘要: |
| Aiming at the variable speed condition, the bearing vibration signal is prone to signal feature aliasing, frequency shift, signal truncation and noise pollution, a fault classification model combining angular resampling, principal component analysis (PCA) and extreme gradient boosting tree (XGBoost) is proposed. Secondly, the time-frequency feature parameters are extracted by principal component analysis (PCA), and the main elements with total contribution greater than 95% are selected as input samples of XGBoost model; finally, the main parameters of XGBoost are tuned by grid search method, and the model is trained by dividing the training set and the test set to verify the accuracy of its fault classification. The results show that the accuracy of fault diagnosis is 96.44%, the running time is shortened by 27.24 s compared with that of the data without dimensionality reduction, and the diagnosis effect after angle resampling is obviously better than that of the diagnosis effect without angle resampling, and the fault recognition rate is improved by more than 7%, which proves that the proposed method can make diagnosis more quickly and accurately. |
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