...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Proceedings of the Institution of Mechanical Engineers, Part C. Journal of mechanical engineering science >Remaining life prognostics of rolling bearing based on relative features and multivariable support vector machine
【24h】

Remaining life prognostics of rolling bearing based on relative features and multivariable support vector machine

机译：基于相关特征和多变量支持向量机的滚动轴承剩余寿命预测

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Life prognostics are an important way to reduce production loss, save maintenance cost and avoid fatal machine breakdowns. Predicting the remaining life of rolling bearing with small samples is a challenge due to lack of enough condition monitoring data. This study proposes a novel prognostics model based on relative features and multivariable support vector machine to meet the challenge. Support vector machine is an effective prediction method for the small samples. However, it only focuses on the univariate time series prognosis and fails to predict the remaining life directly. So multivariable support vector machine is constructed for the life prognostics with many relative features, which are closely linked to the remaining life. Unlike the univariate support vector machine, multivariable support vector machine considers the influences among various variables and excavates the potential information of small samples as much as possible. Besides, relative root mean square with ineffectiveness of the individual difference is used to assess the bearing performance degradation and divided the stages of the whole bearing life. The simulation and run-to-failure experiments are carried out to validate the novel prognostics model. And the results demonstrate that multivariable support vector machine utilizes many kinds of useful information for the precise prediction with practical values.
机译：生命预测是减少生产损失，节省维护成本并避免致命的机器故障的重要方法。由于缺乏足够的状态监测数据，因此，预测带有小样品的滚动轴承的剩余寿命是一项挑战。这项研究提出了一种基于相对特征和多变量支持向量机的新型预测模型，以应对挑战。支持向量机是对小样本的有效预测方法。但是，它仅关注单变量时间序列的预后，而无法直接预测剩余寿命。因此，针对寿命预测构建了具有许多相关特征的多变量支持向量机，这些特征与剩余寿命密切相关。与单变量支持向量机不同，多变量支持向量机考虑了各个变量之间的影响，并尽可能挖掘小样本的潜在信息。此外，用相对个体均方根无效的均方根来评估轴承性能的下降并划分整个轴承寿命的阶段。通过仿真和运行失败实验来验证新型的预测模型。结果表明，多变量支持向量机利用多种有用信息进行具有实际价值的精确预测。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号