| 引用本文: | 王泽洲,陈云翔,蔡忠义,罗承昆.考虑非线性退化与随机失效阈值的剩余寿命预测[J].国防科技大学学报,2020,42(2):177-185.[点击复制] |
| WANG Zezhou,CHEN Yunxiang,CAI Zhongyi,LUO Chengkun.Remaining useful lifetime prediction based on nonlinear degradation processes with random failure threshold[J].Journal of National University of Defense Technology,2020,42(2):177-185[点击复制] |
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| 考虑非线性退化与随机失效阈值的剩余寿命预测 |
| 王泽洲1,陈云翔1,蔡忠义1,罗承昆1,2 |
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(1. 空军工程大学 装备管理与无人机工程学院, 陕西 西安 710051;2. 北京系统工程研究所, 北京 100101)
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| 摘要: |
| 针对广泛存在的非线性退化设备,现有方法尚未考虑随机失效阈值对剩余寿命预测结果的影响。因此,通过对设备性能退化过程进行分析,提出了一种综合考虑非线性退化与随机失效阈值的剩余寿命预测方法。基于Wiener过程构建了考虑个体差异与测量误差的非线性退化模型；基于卡尔曼滤波算法建立状态空间模型以实现对退化状态的在线更新；基于极大似然法估计失效阈值分布系数估计方法,得到随机失效阈值的概率分布；基于随机失效阈值推导出剩余寿命的概率分布,实现对剩余寿命的在线预测。算例研究表明,所提方法可以有效地提升剩余寿命预测的准确性,具备一定工程应用价值。 |
| 关键词: Wiener过程 非线性退化 卡尔曼滤波 随机失效阈值 剩余寿命预测 |
| DOI：10.11887/j.cn.202002024 |
| 投稿日期：2018-10-15 |
| 基金项目:中国博士后科学基金资助项目(2017M623415)；国防科工局技术基础科研计划渠道资助项目(JSZL2016210B001) |
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| Remaining useful lifetime prediction based on nonlinear degradation processes with random failure threshold |
| WANG Zezhou1, CHEN Yunxiang1, CAI Zhongyi1, LUO Chengkun1,2 |
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(1. Equipment Management & UAV Engineering College, Air Force Engineering University, Xi′an 710051, China;2. Beijing Institute of System Engineering, Beijing 100101, China)
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| Abstract: |
| For the nonlinear degradation equipment which widely exists in practice, the current RUL (remaining useful lifetime) prediction methods ignore the effect of random failure threshold. Therefore, the RUL prediction method based on nonlinear degradation processes with random failure threshold was proposed by analyzing equipment′s degradation processes. A nonlinear degradation model based on Wiener process with the individual difference and measurement error was built in this work. Next, the degradation states were updated synchronously by applying the Kalman filtering algorithm and constructing the state-space model. And then, the estimation method of failure threshold distribution parameters based on maximum likelihood estimation was proposed to obtain the probability distribution of the random failure threshold. Finally, an analytical and closed-form RUL distribution based on random failure threshold was derived, and the RUL prediction can be adaptively updated with the available observed data. The case study shows that the presented method can significantly improve the accuracy of RUL prediction and thus it has a certain engineering application value. |
| Key words: Wiener process nonlinear degradation Kalman filtering random failure threshold remaining useful lifetime prediction |
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