北京航空航天大学学报 ›› 2016, Vol. 42 ›› Issue (6): 1236-1243.doi: 10.13700/j.bh.1001-5965.2015.0396

• 论文 • 上一篇    下一篇

变工况条件下基于相似性的剩余使用寿命预测方法

李琪, 高占宝, 李善营, 李宝安   

  1. 北京航空航天大学 自动化科学与电气工程学院, 北京 100083
  • 收稿日期:2015-06-17 出版日期:2016-06-20 发布日期:2016-07-06
  • 通讯作者: 高占宝,Tel.:010-82338693 E-mail:gaozhanbao@buaa.edu.cn E-mail:gaozhanbao@buaa.edu.cn
  • 作者简介:李琪 女,硕士研究生。主要研究方向:设备健康管理与剩余使用寿命预测。E-mail:15210585903@163.com;高占宝 男,讲师,硕士生导师。主要研究方向:计算机测控、复杂系统故障预测与综合健康管理。E-mail:gaozhanbao@bjtu.edu.cn;李善营 男,硕士研究生。主要研究方向:健康管理与故障诊断。E-mail:18810691321@163.com;李宝安 男,副教授,硕士生导师。主要研究方向:无人机健康管理与飞行安全。E-mail:superlba@163.com
  • 基金资助:
    中央高校基本科研业务费专项资金(YWF-14-ZDHXY-16)

Similarity-based remaining useful life prediction method under varying operational conditions

LI Qi, GAO Zhanbao, LI Shanying, LI Baoan   

  1. School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China
  • Received:2015-06-17 Online:2016-06-20 Published:2016-07-06

摘要: 剩余使用寿命(RUL)预测是预测与健康管理(PHM)中的核心环节。提出一种变工况条件下基于相似性的RUL预测方法。结合相似性预测方法无需进行复杂的退化过程建模而能提供合理预测的优势,引入工况即设备工作时所处的环境或操作载荷等因素的影响来提升设备RUL预测准确性。对参考样本建立多工况的设备退化模型提升模型精度,在服役样本相似性度量预测中进行工况的匹配以实现在变工况下的RUL预测。方法能够更准确地描述实际工程中设备的退化过程和个体差异。依据相同准确度标准完成多组基本相似性方法和本文方法的对比实验结果表明,本文方法能够有效提高RUL预测准确度。

关键词: 剩余使用寿命(RUL), 预测, 工况, 相似性, 健康指标

Abstract: Remaining useful life (RUL) prediction is the core task of prognostic and health management (PHM). A similarity-based RUL prediction method under varying operational conditions was presented. Similarity-based RUL prediction method does not need to build a model for entire complex system but can provide reasonable results, which is promising in engineering practice. However, the operational conditions such as different working loads and environmental conditions are not considered for degradation modeling. Therefore, this method combines basic similarity-based method and the effect of operational conditions to achieve better RUL prediction accuracy. Degradation models with different operational conditions were built by training units, and the RUL prediction was achieved by matching corresponding model using the real-time operational conditions of the running unit. The proposed degradation models describe the degradation process more precisely by taking the differences of operational conditions into account. According to the same accuracy standard, multi-group numerical experiments were finished by basic similarity-based method and the proposed method. The result shows the proposed method has a higher accuracy in RUL prediction.

Key words: remaining useful life (RUL), prognostics, operational condition, similarity, health indicator

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