基于混合域相对特征和FOA⁃XGBoost滚动轴承退化评估
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TH17; TP18

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国家自然科学基金资助项目(51775409,51420004);装备预研基金资助项目(6140004030116JW08001);国家重点研发计划资助项目(2017YFF0210504)


Degradation Assessment of Rolling Bearings Based on Mixed Domain Relative Feature and FOA⁃XGBoost Modole
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    摘要:

    针对使用多域特征进行滚动轴承退化评估建模时准确度较低的问题,提出一种基于果蝇优化算法(fruit fly optimization algorithms,简称FOA)集成极限梯度提升树(extreme gradient boosting,简称XGBoost)的轴承退化状态评估方法。提取滚动轴承全寿命周期的时域、频域及时频域等多维特征参数,构建混合域相对特征集,利用相对方均根值初始化轴承退化相应参数,进而利用混合域特征训练XGBoost模型并结合FOA算法对退化评估模型进行参数调优。结果表明:所构建的退化评估模型比常用的支持向量回归(support vactor regerssion,简称SVR)模型在2个数据集上的性能分别提高了27.15%和34.96%,所提方法可以准确有效地评估轴承退化状态。

    Abstract:

    Aiming at deficiency in modeling rolling bearing degradation assessment using multi-domain features, a prediction method is proposed based on the fruit fly optimization algorithm (FOA) and extreme gradient boosting (XGBoost) to estimate the rolling bearing condition. Firstly, the relative feature set in mixed domain is constructed according to the multi-dimensional characteristic parameters such as time domain, frequency domain and time-frequency domain of the whole life cycle of rolling bearings. Then, the relative root mean square value is used to determine the corresponding parameters of bearing degradation, and then the XGBoost model is trained by using mixed domain features, and the parameters of the degradation evaluation model are optimized by using FOA algorithm. The experimental results show that the performance of the degraded evaluation model is 27.15% and 34.96% higher than that of the commonly used support vector regression (SVR) model on the two data sets, respectively. The proposed method can accurately and effectively evaluate the bearing degraded condition.

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  • 在线发布日期: 2021-10-31
  • 出版日期: 2021-10-30
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