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融合多源数据的非线性退化建模与剩余寿命预测

327    2020-02-27

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作者:任子强, 司小胜, 胡昌华, 王玺, 裴洪

作者单位:火箭军工程大学导弹工程学院, 陕西 西安 710025


关键词:复合健康指标;非线性退化模型;贝叶斯参数更新;剩余寿命预测


摘要:

针对目前基于单个传感器剩余寿命预测方法存在预测精度不高的问题,该文提出一种融合多源传感器数据的非线性退化建模与剩余寿命预测方法。该方法包括复合健康指标的构建、模型参数的估计和传感器融合系数的确定,在确定融合系数后,结合设备历史寿命数据与实时监测数据,利用Bayesian参数更新公式推导出设备的剩余寿命概率分布,实现设备的剩余寿命在线预测。最后通过由商用模块化航空推进系统仿真生成的发动机退化数据集进行仿真实验,结果表明该文所提方法能够有效提高设备剩余寿命预测的准确性。


Multi-source data fusion for nonlinear degradation modeling and remaining useful life prediction
REN Ziqiang, SI Xiaosheng, HU Changhua, WANG Xi, PEI Hong
School of Missile Engineering, Rocket Force University of Engineering, Xi'an 710025, China
Abstract: Aiming at the problem that the remaining useful life prediction accuracy is not high based on the single sensor’s signals, the article proposes a remaining useful life prediction method for equipment which is modeled by nonlinear degradation model fusing multi-sensors data. The method includes the construction of a composite health indicator, estimation of model parameters, and determination of sensors’ fusion coefficients. After determining the fusion coefficient, combined with historical life data and real-time monitoring data of equipment, the Bayesian parameter update is used to derive the remaining useful life probability distribution, and then realize the online remaining useful life prediction of a equipment. Finally, the simulation experiment is carried out by the engine degradation data set generated by commercial modular aero-propulsion system simulation. The results show that the proposed method can effectively improve the accuracy of engine’s remaining useful life prediction.
Keywords: composite health indicator;nonlinear degradation model;Bayesian parameter update;remaining useful life prediction
2020, 46(2):1-8  收稿日期: 2019-05-14;收到修改稿日期: 2019-07-02
基金项目: 国家自然科学基金(61833016,61573365)
作者简介: 任子强(1995-),男,重庆市人,硕士研究生,专业方向为故障诊断与寿命预测
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