A Prediction Method for Remaining Life of Rolling Bearing Using Improved Regression Support Vector Machine

doi:10.7652/xjtuxb202203020

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A Prediction Method for Remaining Life of Rolling Bearing Using Improved Regression Support Vector Machine

更新时间：2025-07-09

- A Prediction Method for Remaining Life of Rolling Bearing Using Improved Regression Support Vector Machine
- Vol. 56, Issue 3, Pages: 197-205(2022)
- 作者机构：
  
  1. 三峡大学水电机械设备设计与维护湖北省重点实验室,湖北,宜昌,443002
  2. 三峡大学机械与动力学院,湖北,宜昌,443002
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202203020
  CLC： TH133.33
- Online First：10 March 2022，
  
  Published：2022
- 稿件说明：
移动端阅览
A Prediction Method for Remaining Life of Rolling Bearing Using Improved Regression Support Vector Machine[J]. 2022, 56(3): 197-205.
DOI：

A Prediction Method for Remaining Life of Rolling Bearing Using Improved Regression Support Vector Machine[J]. 2022, 56(3): 197-205. DOI： 10.7652/xjtuxb202203020.

摘要

为准确评估滚动轴承运行状态、预测其性能退化趋势以及剩余寿命

提出一种改进回归型支持向量机(SVR)的滚动轴承寿命预测方法。提取轴承信号的时域和时频域特征

通过主成分分析(PCA)方法将特征指标融合成一个归一化综合指标来表征轴承运行状态; 利用特征指标和综合指标构建训练和预测向量数据集

结合差分进化灰狼群算法(DEGWO)确定最优惩罚参数和径向基函数(RBF)核参数并构建回归型支持向量机模型; 将预测数据集输入到DEGWO算法优化的SVR模型中得到轴承状态评估指标的预测值

实现轴承剩余寿命的预测。利用IEEE PHM 2012数据集验证所提方法的有效性

并将其结果与灰狼群算法(GWO)优化的SVR、网格搜索算法(GSA)优化的SVR和长短期记忆神经网络(LSTM)模型所得结果进行对比分析。仿真结果表明:与其他方法相比

采用所提方法得到的轴承剩余寿命预测均方误差分别降低了44.74%、66.67%、77.27%

决定系数则分别提高了7.25%、20.72%、11.94%

该结果说明了所提方法在轴承剩余寿命预测应用方面的优越性。

Abstract

A prediction method of rolling bearing life based on improved regression support vector machine(SVR)is proposed to accurately evaluate the running state of rolling bearings and predict their performance degradation trend and remaining life. The time domain and time-frequency domain features of bearing signals are extracted

and the feature indexes are fused into a normalized comprehensive index by principal component analysis(PCA)method to characterize the running state of the bearing. Data sets of training and prediction vectors are constructed by using the feature indexes and the comprehensive indexe

the optimal penalty parameters and RBF kernel parameters are determined by the differential evolution grey wolf optimizer(DEGWO)

and the SVR model is constructed. The prediction data set is input into the SVR model optimized by DEGWO algorithm to obtain the predicted value of the bearing condition evaluation index

and the remaining life of the bearing is predicted. The validity of the proposed method is verified using the IEEE PHM 2012 Dataset. The advantage of the proposed method is examined by comparing the prediction result with those obtained by the SVR model optimized by grey wolf optimizer(GWO)

the SVR model optimized by grid search algorithm(GSA)

and the long short-term memory networks(LSTM)model. Simulation results show that compared with other methods

the MSE values of bearing life prediction obtained by the proposed method is reduced by 44.74%

66.67% and 77.27% respectively

and the determination coefficient is increased by 7.25%

20.72% and 11.94% respectively. These results demonstrate the superiority of the proposed method in the application of bearing remaining life prediction.

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references

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