Prediction Method for Remaining Useful Life of Standby Systems Considering Degradation of Spare Parts

ZHANG Jialing; ZHANG Jianxun; DU Dangbo; ZHANG Zhengxin; SI Xiaosheng

doi:10.7652/xjtuxb202412017

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Prediction Method for Remaining Useful Life of Standby Systems Considering Degradation of Spare Parts

更新时间：2025-07-28

- Prediction Method for Remaining Useful Life of Standby Systems Considering Degradation of Spare Parts
- Vol. 58, Issue 12, Pages: 175-185(2024)
- 作者机构：
  
  火箭军工程大学智剑实验室,西安,710025
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202412017
  CLC： TH17
- Online First：10 December 2024，
  
  Published：2024
- 稿件说明：
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ZHANG Jialing, ZHANG Jianxun, DU Dangbo, et al. Prediction Method for Remaining Useful Life of Standby Systems Considering Degradation of Spare Parts[J]. 2024, 58(12): 175-185.
DOI：

ZHANG Jialing, ZHANG Jianxun, DU Dangbo, et al. Prediction Method for Remaining Useful Life of Standby Systems Considering Degradation of Spare Parts[J]. 2024, 58(12): 175-185. DOI： 10.7652/xjtuxb202412017.

摘要

针对非理想贮存条件下备件退化甚至失效

导致运行设备与备件组成的贮备系统实际运行寿命难预测的问题

提出了一种考虑备件性能退化的贮备系统剩余寿命预测方法。基于维纳过程分别建立了系统在运行和贮存条件下的随机退化模型

引入了随机效用刻画系统退化过程的个体差异性

推导出备件的时变退化状态分布及失效概率; 结合运行条件下与贮存条件下的退化模型

推导出了首达时间意义下贮备系统剩余寿命分布的迭代求解方法; 基于极大似然估计和贝叶斯理论

提出了模型参数的离线辨识和在线更新策略。通过数值仿真数据和某型号陀螺仪的实际数据进行实验验证

实验结果表明:所提方法的剩余寿命期望理论值与数值仿真结果高度吻合

且理论值与近似值的平均相对误差为0.030 3 h

验证了所提方法的理论正确性; 同时

所提方法能够有效表征备件退化对于剩余寿命预测的影响

剩余寿命与真实剩余寿命的平均均方根误差为37.645 3 h

比不考虑随机效用的方法降低了1.004 h

预测精度显著提高。该方法为贮备系统剩余寿命预测提供了有效途径。

Abstract

To address the challenge of predicting the actual operational life of a standby system

comprising operating equipment and spare parts

due to the degradation or failure of spare parts under non-ideal storage conditions

a method for estimating the residual useful life of standby systems while considering spare part degradation is proposed. Stochastic degradation models for the system under both operational and storage conditions are developed using Wiener processes. Individual differences in system degradation are accounted for through stochastic utility

enabling the derivation of the time-varying degradation state distribution of spare parts and failure probabilities. By integrating degradation models for operational and storage conditions

an iterative approach for determining the distribution of the system's residual useful life concerning the first reach time is established. An offline parameter identification and online updating strategy

employing maximum likelihood estimation and Bayesian theory

are introduced. Experimental validation is performed utilizing numerical simulations and empirical data from a specific model gyroscope. The experimental results reveal that the expected theoretical values of residual useful life generated by the proposed method closely align with numerical simulations

with an average relative error of 0.030 3 h between theoretical and approximate values

validating the method's theoretical accuracy. Moreover

the impact of spare part degradation on residual useful life predictions is effectively assessed by the method

with an average root mean square error of 37.645 3 h between predicted and actual residual useful life achieved

representing a reduction of 1.004 h compared to approaches neglecting stochastic utility and significantly enhancing prediction accuracy. A robust means of forecasting the residual useful life of standby systems is offered by this method.

关键词

Keywords

references

ZIO E. Prognostics and health management(PHM): where are we and where do we(need to)go in theory and practice [J]. Reliability Engineering & System Safety, 2022, 218: 108119.

HU Yang, MIAO Xuewen, SI Yong, et al. Prognostics and health management: a review from the perspectives of design, development and decision [J]. Reliability Engineering System Safety, 2022, 217: 108063.

曾声奎, PECHT M G, 吴际. 故障预测与健康管理(PHM)技术的现状与发展 [J]. 航空学报, 2005, 26(5): 626-632.

ZENG Shengkui, PECHTM G, WU Ji. Status and perspectives of prognostics and health management technologies [J]. Acta Aeronautica et Astronautica Sinica, 2005, 26(5): 626-632.

周东华, 魏慕恒, 司小胜. 工业过程异常检测、寿命预测与维修决策的研究进展 [J]. 自动化学报, 2013, 39(6): 711-722.

ZHOU Donghua, WEI Muheng, SI Xiaosheng. A survey on anomaly detection, life prediction and maintenance decision for industrial processes [J]. Acta Automatica Sinica,2013, 39(6): 711-722.

FERREIRA C, GONÇALVES G. Remaining useful life prediction and challenges: a literature review on the use of machine learning methods [J]. Journal of Manufacturing Systems, 2022, 63: 550-562.

SI Xiaosheng, WANG Wenbin, HU Changhua, et al. Remaining useful life estimation: a review on the statistical data driven approaches [J]. European Journal of Operational Research, 2011, 213(1): 1-14.

刘雨蒙, 郑旭, 田玲, 等. 基于时序图推理的设备剩余使用寿命预测 [J]. 自动化学报, 2024, 50(1): 76-88.

LIU Yumeng, ZHENG Xu, TIAN Ling, et al. Remaining useful life estimation of facilities based on reasoning over temporal graphs [J]. Acta Automatica Sinica, 2024, 50(1): 76-88.

ZHAO Huimin, LIU Haodong, JIN Yang, et al. Feature extraction for data-driven remaining useful life prediction of rolling bearings [J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-10.

李乃鹏, 蔡潇, 雷亚国, 等. 一种融合多传感器数据的数模联动机械剩余寿命预测方法 [J]. 机械工程学报, 2021, 57(20): 29-37.

LI Naipeng, CAI Xiao, LEI Yaguo, et al. A model-data-fusion remaining useful life prediction method with multi-sensor fusion for machinery [J]. Journal of Mechanical Engineering, 2021, 57(20): 29-37.

陈童, 谢经伟, 狄鹏, 等. 考虑两种失效竞争的多状态冷贮备系统可靠性模型 [J]. 航空学报, 2019, 40(3): 110-125.

CHEN Tong, XIE Jingwei, DI Peng, et al. Reliability analysis of multi-state cold standby system with two competingfailures [J]. Acta Aeronautica et Astronautica Sinica, 2019, 40(3): 110-125.

金海波, 赵欣越, 桑雨. 双贮备系统冷/温/热贮备模型的优化选择研究 [J]. 自动化学报, 2023, 49(9): 2003-2018.

JIN Haibo, ZHAO Xinyue, SANG Yu. Research on optimal selection for cold/warm/hot-standby patterns of dual-standby systems [J]. Acta Automatica Sinica, 2023, 49(9): 2003-2018.

ZHAO Youhu, ZIO E, FU Guicui. Remaining storage life prediction for an electromagnetic relay by a particle filtering-based method [J]. Microelectronics Reliability, 2017, 79: 221-230.

ZHANG Jianchun, MA Xiaobing, CHENG Bo, et al. Storage life modeling and analysis for contacting slip ring based on physics of failure [J]. IEEE Transactions on Components, Packaging and Manufacturing Technology, 2017, 7(12): 1969-1980.

JIA Heping, DING Yi, PENG Rui, et al. Reliability evaluation for demand-based warm standby systems considering degradation process [J]. IEEE Transactions on Reliability, 2017, 66(3): 795-805.

JIANG Yunpeng, CHEN Maoyin, ZHOU Donghua. Joint optimization of preventive maintenance and inventory policies for multi-unit systems subject to deteriorating spare part inventory [J]. Journal of Manufacturing Systems, 2015, 35: 191-205.

MA Xiaoyang, LIU Bin, YANG Li, et al. Reliability analysis and condition-based maintenance optimization for a warm standby cooling system [J]. Reliability Engineering System Safety, 2020, 193: 106588.

ELWANY A H, GEBRAEEL N Z. Sensor-driven prognostic models for equipment replacement and spare parts inventory [J]. IIE Transactions, 2008, 40(7): 629-639.

ZHANG Jianxun, SI Xiaosheng, DU Dangbo, et al. A novel iterative approach of lifetime estimation for standby systems with deteriorating spare parts [J]. Reliability Engineering System Safety, 2020, 201: 106960.

ZHANG Zhengxin, ZHANG Jianxun, DU Dangbo, et al. A lifetime estimation method for multi-component degrading systems with deteriorating spare parts [J]. Reliability Engineering System Safety, 2023, 238: 109427.

任锦程, 郑建飞, 胡昌华, 等. 融合Transformer网络与维纳过程的贮备系统寿命预测 [J/OL]. 哈尔滨工程大学学报.(2023-09-08)[2024-04-01]. http://kns.cnki.net/kcms/detail/23.1390.u.20230906.1037.002.html.

REN Jincheng, ZHENG Jianfei, HU Changhua, et al. Life prediction of standby system integrating Transformer network and Wiener process [J/OL]. Journal of Harbin Engineering University.(2023-09-08)[2024-04-01]. http://kns.cnki.net/kcms/detail/23.1390.u.20230906.1037.002.html.

雷亚国, 韩天宇, 王彪, 等. XJTU-SY滚动轴承加速寿命试验数据集解读 [J]. 机械工程学报, 2019, 55(16): 1-6.

LEI Yaguo, HAN Tianyu, WANG Biao, et al. XJTU-SY rolling element bearing accelerated life test datasets: a tutorial [J]. Journal of Mechanical Engineering, 2019, 55(16): 1-6.

李天梅, 司小胜, 刘翔, 等. 大数据下数模联动的随机退化设备剩余寿命预测技术 [J]. 自动化学报, 2022, 48(9): 2119-2141.

LI Tianmei, SI Xiaosheng, LIU Xiang, et al. Data-model interactive remaining useful life prediction technologies for stochastic degrading devices with big data [J]. Acta Automatica Sinica, 2022, 48(9): 2119-2141.

王凤飞, 唐圣金, 孙晓艳, 等. 考虑随机效应的多源信息融合剩余寿命预测 [J]. 北京航空航天大学学报, 2023, 49(11): 3075-3085.

WANG Fengfei, TANG Shengjin, SUN Xiaoyan, et al. Remaining useful life prediction based on multi source information with considering random effects [J]. Journal of Beijing University of Aeronautics and Astronautics, 2023, 49(11): 3075-3085.

WANG Fengfei, TANG Shengjin, SUN Xiaoyan, et al. Remaining useful life prediction based on nonlinear random coefficient regression model with fusing failure time data [J]. Journal of Systems Engineering and Electronics, 2023, 34(1): 247-258.

SI Xiaosheng, WANG Wenbin, CHEN Maoyin, et al. A degradation path-dependent approach for remaining useful life estimation with an exact and closed-form solution [J]. European Journal of Operational Research, 2013, 226(1): 53-66.

SI Xiaosheng, HU Changhua, KONG Xiangyu, et al. A residual storage life prediction approach for systems with operation state switches [J]. IEEE Transactions on Industrial Electronics, 2014, 61(11): 6304-6315.

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