基于两阶段自适应<b>Wiener</b>过程的剩余寿命预测方法

董青; 郑建飞; 胡昌华; 李冰; 牟含笑

doi:10.16383/j.aas.c210057

基于两阶段自适应Wiener过程的剩余寿命预测方法

doi: 10.16383/j.aas.c210057

董青^1,,
郑建飞^1,,
胡昌华^1,,
李冰^2,,
牟含笑^1,

1.
火箭军工程大学导弹工程学院西安 710025
2.
火箭军装备部驻西安地区第一军事代表室西安 710000

基金项目: 国家自然科学基金(61773386, 61833016, 61922089, 62073336), 陕西省自然科学基金 (2020JM-360)资助

详细信息

作者简介:
董青：火箭军工程大学硕士研究生. 主要研究方向为预测与健康管理, 预测维护. E-mail: 18756528162@163.com

郑建飞：火箭军工程大学副教授. 主要研究方向为预测与健康管理, 可靠性和预测维护. 本文通信作者. E-mail: zjf302@126.com

胡昌华：火箭军工程大学教授, 长江学者. 主要研究方向包括故障诊断和预测, 寿命预测和容错控制. E-mail: hch666@163.com

李冰：火箭军装备部驻西安地区第一军事代表室工程师. 主要研究方向为可靠性和预测维护. E-mail: yanyunsheng-518@163.com

牟含笑：火箭军工程大学硕士研究生. 主要研究方向为预测与健康管理, 预测维护和深度神经网络. E-mail: 18730269356@163.com

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出版历程
- 收稿日期: 2021-01-19
- 修回日期: 2021-03-29
- 网络出版日期: 2021-06-14
- 刊出日期: 2022-02-18

Remaining Useful Life Prognostic Method Based on Two-stage Adaptive Wiener Process

1.
College of Missile Engineering, Rocket Force University of Engineering, Xi＇ an 710025
2.
The First Military Representative Office of the Rocket Force Equipment Department in Xi＇ an, Xi＇ an 710000

Funds: Supported by National Natural Science Foundation of China (61773386, 61833016, 61922089, 62073336) and Natural Science Foundation of Shaanxi Province (2020JM-360)

More Information

Author Bio:
DONG Qing　Master student at the Rocket Force University of Engineering. His research interest covers prognostics and health management, and predictive maintenance

ZHENG Jian-Fei　Associate professor at the Rocket Force University of Engineering. His research interest covers prognostics and health management, reliability, and predictive maintenance. Corresponding author of this paper

HU Chang-Hua　Professor at the Rocket Force University of Engineering. His research interest covers fault diagnosis and prediction, life prognosis, and fault tolerant control

LI Bing　Engineer at the First Military Representative Office of the Rocket Force Equipment Department in Xi＇an. His research interest covers reliability and predictive maintenance

MU Han-Xiao　Master student at the Rocket Force University of Engineering. Her research interest covers prognostics and health management, predictive maintenance and deep neural networks

摘要

摘要: 针对退化过程呈现两阶段特征的一类随机退化设备, 现有剩余寿命预测方法不适用于测量间隔分布不均匀、监测数据的测量频率与历史数据频率不一致的情况, 并且忽略了自适应漂移的可变性. 鉴于此, 提出了一种新的考虑个体差异性的两阶段自适应Wiener过程剩余寿命预测模型与方法. 首先, 基于自适应Wiener过程分阶段构建随机退化模型, 在首达时间意义下推导出寿命和剩余寿命解析式. 然后, 结合Kalman滤波技术和期望最大化算法进行参数自适应更新, 同时利用赤池信息准则实现退化模型变点的辨识. 最后, 通过蒙特卡洛仿真和锂电池实例, 验证了本文所提方法的有效性和实用价值.
- 剩余寿命 /
- 两阶段自适应Wiener过程 /
- 期望最大化算法 /
- 赤池信息准则
Abstract: For a class of stochastic degradation equipments with two-stage degradation process, the existing remaining useful life (RUL) prediction method is not suitable for the situation that non-uniform measurement interval, inconsistent measurement frequency of monitoring data and historical data, and ignored it the variability of adaptive drift. In view of this, the paper proposes a new RUL prediction model and method which considers individual differences two-stage adaptive Wiener process. Firstly, the stochastic degradation model is constructed in stages based on the adaptive Wiener process, and the analytical expressions of life and RUL are derived in the sense of first arrival time. Then, the Kalman filtering technology and expectation maximization (EM) algorithm are combined to update the parameters adaptively, and the Schwarz information criterion is used to identify the change points of the degraded model. Finally, the effectiveness and practical value of the proposed method are verified by an example of lithium-ion battery data and the Monte Carlo simulation.
- Remaining useful life /
- two-stage adaptive Wiener process /
- EM algorithm /
- Schwarz information criterion

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图 1 两种模型RUL预测结果

Fig. 1 RUL prediction results of the two models

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图 2 两种模型RUL预测绝对误差

Fig. 2 Absolute error of RUL prediction of the two models

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图 3 锂电池容量退化

Fig. 3 Lithium battery capacity degradation

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图 4 CS2-37锂电池SIC值

Fig. 4 SIC value of CS2-37 lithium battery

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图 5 第一阶段模型参数更新

Fig. 5 The first stage of model parameter updating

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图 6 第二阶段模型参数更新

Fig. 6 The second stage of model parameter updating

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图 7 CS2-37模型拟合效果

Fig. 7 Fitting effect of CS2-37 model

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图 8 CS2-37的RUL预测结果

Fig. 8 RUL prediction results of CS2-37

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图 9 RUL预测绝对误差

Fig. 9 Absolute error of RUL prediction

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图 10 $\alpha {\rm{ - }}\beta $性能图

Fig. 10 $\alpha {\rm{ - }}\beta $ performance chart

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表 1 不同监测点相对误差的比较结果

Table 1 Comparison results of relative error at different monitoring points

方法不同监测点相对误差
20 40 60 80

提出模型 20.8 % 19.6 % 15.2 % 12.3 %
Zhang等^[19]模型 27.2 % 25.3 % 20.1 % 17.6 %

下载: 导出CSV

表 2 不同寿命分位点相对误差的比较结果

Table 2 Comparison results of relative error at different life quantiles

方法寿命分位点相对误差
35 % 55 % 75 % 95 %

提出模型 6.04 7.65 7.91 0.66
Zhang 等^[19]模型 11.4 9.37 9.68 1.46

下载: 导出CSV

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基于两阶段自适应Wiener过程的剩余寿命预测方法

doi: 10.16383/j.aas.c210057

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Remaining Useful Life Prognostic Method Based on Two-stage Adaptive Wiener Process

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目录

方法	不同监测点相对误差
方法	20	40	60	80
提出模型	20.8 %	19.6 %	15.2 %	12.3 %
Zhang等^[19]模型	27.2 %	25.3 %	20.1 %	17.6 %

方法	寿命分位点相对误差
方法	35 %	55 %	75 %	95 %
提出模型	6.04	7.65	7.91	0.66
Zhang 等^[19]模型	11.4	9.37	9.68	1.46

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基于两阶段自适应Wiener过程的剩余寿命预测方法

doi: 10.16383/j.aas.c210057

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Remaining Useful Life Prognostic Method Based on Two-stage Adaptive Wiener Process

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