首页> 外文会议>International Conference on Computing and Data Science >An Improved SVR-FCM Method for Remaining Useful Life Prediction of Aircraft Engines
【24h】

An Improved SVR-FCM Method for Remaining Useful Life Prediction of Aircraft Engines

机译:一种改进的SVR-FCM方法,用于剩余的飞机发动机使用寿命预测

获取原文

摘要

Predicting the remaining useful life (RUL) remains an important part in prognostics and health management (PHM) discipline. But the complexity of machine system and the noise in data make the prediction full of uncertainty and diversity. On the other hand, the traditional statistic methods are not capable for this kind of problems because of the limited number of data and previous knowledge. In this case, the machine learning methods are widely used in PHM field, since they are data-driven approaches which enable the model to determine the states without considering a homogeneous pattern. In this paper, the kernel principal component analysis and sliding window method are introduced for extracting the main features of the raw datasets. Then, support vector machine is used to predict the RUL of aircraft engines using data from C-MAPSS. The real RUL is uncertain, based on the value of features, so the fuzzy theory is introduced to the model to get the prediction interval of RUL. This paper also provides the performance running on dataset FDOOI to compare with prior approaches.
机译:预测剩余的使用寿命(RUL)仍然是预后和健康管理(PHM)纪律的重要组成部分。但是机器系统的复杂性和数据中的噪声使得预测充满不确定性和多样性。另一方面,由于数据数量和以前的知识有限,传统的统计方法对这种问题没有必要。在这种情况下,机器学习方法广泛用于PHM字段,因为它们是数据驱动的方法,其使模型能够在不考虑均匀模式的情况下确定状态。在本文中,引入了内核主成分分析和滑动窗口方法,用于提取原始数据集的主要特征。然后,支持向量机用于预测使用C-MAPS的数据来预测飞机引擎的RUL。基于特征的价值,真正的RUL是不确定的,因此模糊理论被引入模型以获得RUL的预测间隔。本文还提供了在数据集FDOOI上运行的性能,以与先前的方法进行比较。

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