Regression Trend Prediction of Rolling Bearing Performance based on Integrated Soft Competition ART

Zhao Qiankun; Wan Xiaojin; Xu Zengbing; Wang Kai; Li Qinglei

doi:10.16578/j.issn.1004.2539.2018.01.028

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Regression Trend Prediction of Rolling Bearing Performance based on Integrated Soft Competition ART

更新时间：2022-10-20

- Regression Trend Prediction of Rolling Bearing Performance based on Integrated Soft Competition ART
- Vol. 42, Issue 1, Pages: 131-136(2018)
- 作者机构：
  
  1. 武汉理工大学现代汽车零部件技术湖北省重点实验室
  2. 武汉理工大学汽车工程学院
  3. 武汉科技大学机械自动化学院
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2018.01.028
  CLC：
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[1]赵乾坤,万小金,徐增丙,王凯,李清蕾.基于集成软竞争ART的滚动轴承性能退化趋势预测[J].机械传动,2018,42(01):131-136.

Zhao Qiankun, Wan Xiaojin, Xu Zengbing, et al. Regression Trend Prediction of Rolling Bearing Performance based on Integrated Soft Competition ART. [J]. 42(1):131-136(2018)
[1]赵乾坤,万小金,徐增丙,王凯,李清蕾.基于集成软竞争ART的滚动轴承性能退化趋势预测[J].机械传动,2018,42(01):131-136. DOI： 10.16578/j.issn.1004.2539.2018.01.028.

Zhao Qiankun, Wan Xiaojin, Xu Zengbing, et al. Regression Trend Prediction of Rolling Bearing Performance based on Integrated Soft Competition ART. [J]. 42(1):131-136(2018) DOI： 10.16578/j.issn.1004.2539.2018.01.028.

摘要

为了提高滚动轴承性能预测的准确性和稳定性,提出了以软竞争ART-RBF的集成预测模型和置信度CV值相结合的预测方法。将软竞争ART引入RBF神经网络,建立软竞争ART-RBF神经网络预测模型。结合加权平均技术,建立集成软竞争ART-RBF神经网络预测模型。并通过自组织映射（SOM）网络,获取具有丰富故障信息的置信度（CV）值,作为表征滚动轴承性能退化的综合指标。最后,利用滚动轴承加速疲劳试验获取的加速度信号对上述方法进行验证。结果表明,该方法可有效提高滚动轴承退化趋势预测的准确性和稳定性。

Abstract

In order to improve the accuracy and stability of rolling bearing performance prediction,a prediction method combining soft predictive ART-RBF integrated forecasting model and confidence CV value is proposed. The soft ART is introduced into the RBF neural network to establish the soft ART-RBF neural network prediction model. Combining with weighted average technology,the establishment of integrated soft ART-RBF neural network prediction model is carried out. And the confidence degree（CV） value with rich fault information is obtained through the self-organizing map（SOM） network as a comprehensive index to characterize the degradation of rolling bearing performance. Finally,the above method is verified by the acceleration signal obtained by the accelerated fatigue test of the rolling bearing. The results show that the method can effectively improve the accuracy and stability of the prediction of the degradation trend of rolling bearings.

关键词

软竞争ART-RBF自组织映射网络置信度值滚动轴承预测

Keywords

Soft ART-RBFSelf-organizing mapping networkConfidence valueRolling bearingPrediction

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