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国外文献 > 1.轴承 > 4. 设计与分析 > 设计方法

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标 题： A Hybrid Approach for Predicting the Remaining Useful Life of Bearings Based on the RReliefF Algorithm and Extreme Learning Machine
基于RReliefF算法与极端学习机的轴承剩余使用寿命预测混合方法
作 者： Wang, Sen-Hui
Kang, Xi
Wang, Cheng
Ma, Tian-Bing
He, Xiang
Yang, Ke
关键词： Bearing degradation  [轴承劣化]
remaining useful life estimation  [ 剩余使用寿命估算]
RReliefF feature selection  [ RReliefFF 特征选择]
extreme learning machine  [ 极端学习机器]
FAULT-DIAGNOSIS  [ 故障诊断]
REGRESSION  [ 回归]
MODEL  [ 型号]
出版年： 2024
期刊名： Computer Modeling in Engineering & Sciences
工程与科学中的计算机建模
年卷期： 2024, vol.140, no.2
摘 要： Accurately predicting the remaining useful life (RUL) of bearings in mining rotating equipment is vital for mining enterprises. This research aims to distinguish the features associated with the RUL of bearings and propose a prediction model based on these selected features. This study proposes a hybrid predictive model to assess the RUL of rolling element bearings. The proposed model begins with the pre-processing of bearing vibration signals to reconstruct sixty time-domain features. The hybrid model selects relevant features from the sixty time-domain features of the vibration signal by adopting the RReliefF feature selection algorithm. Subsequently, the extreme learning machine (ELM) approach is applied to develop a predictive model of RUL based on the optimal features. The model is trained by optimizing its parameters via the grid search approach. The training datasets are adjusted to make them most suitable for the regression model using the cross-validation method. The proposed hybrid model is analyzed and validated using the vibration data taken from the public XJTU-SY rolling element-bearing database. The comparison is constructed with other traditional models. The experimental test results demonstrated that the proposed approach can predict the RUL of bearings with a reliable degree of accuracy.

准确预测采矿旋转设备中轴承的剩余使用寿命（RUL）对采矿企业至关重要。本研究旨在识别与轴承RUL相关的特征，并基于这些选定特征提出预测模型。本文提出一种混合预测模型，用于评估滚动轴承的RUL。该模型首先对轴承振动信号进行预处理，重构出六十个时域特征。随后，采用RReliefF特征选择算法从振动信号的六十个时域特征中筛选出相关特征。接着，应用极端学习机（ELM）方法，基于最优特征构建RUL预测模型。模型通过网格搜索法优化参数进行训练，并利用交叉验证方法调整训练数据集，使其最适用于回归模型。所提出的混合模型使用来自公共XJTU-SY滚动轴承数据库的振动数据进行分析和验证，并与其它传统模型进行对比。实验测试结果表明，所提出的方法能够以可靠的准确度预测轴承的剩余使用寿命。
中文内容为网络AI翻译，专业术语未必准确，仅供参考。

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