基于比例加速退化建模的单台机载设备剩余寿命自适应预测

doi:10.12305/j.issn.1001-506X.2021.11.42

系统工程与电子技术 ›› 2021, Vol. 43 ›› Issue (11): 3405-3412.doi: 10.12305/j.issn.1001-506X.2021.11.42

• 可靠性 • 上一篇

基于比例加速退化建模的单台机载设备剩余寿命自适应预测

蔡忠义¹, 王泽洲^2,*, 陈云翔¹, 项华春¹, 王莉莉¹

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. 中国人民解放军93920部队, 陕西汉中 723200

收稿日期:2021-02-01 出版日期:2021-11-01 发布日期:2021-11-12
通讯作者: 王泽洲
作者简介:蔡忠义(1988—), 男, 副教授, 博士, 主要研究方向为装备可靠性评估、剩余寿命预测|王泽洲(1992—), 男, 工程师, 博士, 主要研究方向为装备可靠性评估、剩余寿命预测|陈云翔(1962—), 男, 教授, 博士研究生导师, 博士, 主要研究方向为装备可靠性评估、装备维修保障研究|项华春(1980—), 男, 教授、硕士研究生导师, 博士, 主要研究方向为装备可靠性评估、装备维修保障|王莉莉(1983—), 女, 副教授、硕导, 主要研究方向为装备维修保障、作战效能评估研究
基金资助:
国家自然科学基金(71901216)

Adaptive prediction of remaining useful lifetime for the single airborne equipment based on the proportional accelerated degradation modeling

Zhongyi CAI¹, Zezhou WANG^2,*, Yunxiang CHEN¹, Huachun XIANG¹, Lili WANG¹

1. Equipment Management & UAV Engineering College, Air Force Engineering University, Xi'an 710051, China
2. Unit 93920 of the PLA, Hanzhong 723200, China

Received:2021-02-01 Online:2021-11-01 Published:2021-11-12
Contact: Zezhou WANG

摘要/Abstract

摘要：

针对现有机载设备剩余寿命(remaining useful lifetime, RUL)预测方法在新研单一样本条件下, 无法应用于加速退化试验场景的问题, 本文基于比例关系模型提出了一种加速退化场景下适用于单个试验样本的自适应RUL预测方法。首先, 依据加速退化环境下Wiener过程存在的漂移/扩散系数比例关系, 构建考虑设备个体差异与测量误差的非线性随机退化模型; 其次, 针对加速退化试验存在单一受试样本的情况, 提出了基于期望最大和卡尔曼滤波联合算法的参数自适应估计方法; 然后, 基于卡尔曼滤波原理在线更新目标设备的退化状态, 并推导出设备剩余寿命的概率密度函数; 最后, 通过对单台行波管加速退化实测数据进行分析, 验证了方法的正确性和优势。

关键词: Wiener过程, 加速退化建模, 比例关系, 剩余寿命, 自适应预测

Abstract:

Aiming at the problem that the existing prediction methods of remaining useful lifetime (RUL) for the airborne equipment cannot be applied to the accelerated degradation test scenarios under the condition of a single sample of the new research, this paper proposes an adaptive prediction method of RUL for the single equipment under the accelerated degradation scenario based on the proportional relationship model. Firstly, according to the ratio relationship of drift and diffusion coefficient in the Wiener process under accelerated degradation environment, a nonlinear stochastic degradation model considering individual differences and measurement errors is established. Secondly, for the situation that the accelerated degradation test has only a single sample, a parameter adaptive estimation method based on the joint algorithm of expectation maximum and Kalman filter is proposed. Then, the Kalman filter principle is used to online update the degradation state, and the probability density function of RUL is derived. Finally, through the analysis of the measured data for a single traveling wave tube under accelerated degradation test, the validity and advantages of this method are verified.

Key words: Wiener process, accelerated degradation modeling, proportion relationship, remaining useful lifetime, adaptive prediction

中图分类号:

TB114.3

蔡忠义, 王泽洲, 陈云翔, 项华春, 王莉莉. 基于比例加速退化建模的单台机载设备剩余寿命自适应预测[J]. 系统工程与电子技术, 2021, 43(11): 3405-3412.

Zhongyi CAI, Zezhou WANG, Yunxiang CHEN, Huachun XIANG, Lili WANG. Adaptive prediction of remaining useful lifetime for the single airborne equipment based on the proportional accelerated degradation modeling[J]. Systems Engineering and Electronics, 2021, 43(11): 3405-3412.

图/表 6

参考文献 19

1	DAWN A , JOO H C , HO K N . Prediction of remaining useful life under different conditions using accelerated life testing data[J]. Journal of Mechanical Science & Technology, 2018, 32 (6): 2497- 2507.
2	KIM M Y , CHU D J , LEE Y K , et al. Residual lifetime assessment of cold-reheater pipe in coal-fired power plant through accelerated degradation test[J]. Reliability Engineering & System Safety, 2019, 188, 330- 335.
3	WHITMORE G A , SCHENKELBERG F . Modelling accelerated degradation data using Wiener diffusion with a time scale transformation[J]. Lifetime Data Analysis, 1997, 3 (1): 27- 45. doi: 10.1023/A:1009664101413
4	LIAO H , ELSAYED E A . Reliability inference for field conditions from accelerated degradation testing[J]. Naval Research Logistics, 2006, 53 (6): 576- 587. doi: 10.1002/nav.20163
5	李瑞, 汪立新, 刘刚, 等. 基于加速退化模型的加速度计非线性特征分析及贮存寿命预测[J]. 中国惯性技术学报, 2014, 22 (1): 125- 130.
	LI R , WANG L X , LIU G , et al. Nonlinear characteristic analy- sis and storage life forecast for accelerometer based on accelerate degradation model[J]. Journal of Chinese Inertial Technology, 2014, 22 (1): 125- 130.
6	盖炳良, 滕克难, 王浩伟, 等. 基于加速因子不变原则的加速度计可靠性分析[J]. 中国惯性技术学报, 2018, 26 (6): 835- 840.
	GAI B L , TENG K N , WANG H W , et al. Reliability analysis for accelerometers based on invariant principle of acceleration factor[J]. Journal of Chinese Inertial Technology, 2018, 26 (6): 835- 840.
7	唐圣金, 郭晓松, 周召发, 等. 步进应力加速退化试验的建模与剩余寿命估计[J]. 机械工程学报, 2014, 50 (16): 33- 40.
	TANG S J , GUO X S , ZHOU Z F , et al. Step stress accele-rated degradation process modeling and remaining useful life estimation[J]. Journal of Mechanical Engineering, 2014, 50 (16): 33- 40.
8	HAO S , YANG J , BERENGUER C . Nonlinear step-stress accelerated degradation modeling considering three sources of variability[J]. Reliability Engineering and System Safety, 2018, 172 (1): 207- 215.
9	CAI Z Y , CHEN Y X , ZHANG Q , et al. Residual lifetime prediction model of nonlinear accelerated degradation data with measurement error[J]. Journal of Systems Engineering and Electronics, 2017, 28 (5): 1028- 1038. doi: 10.21629/JSEE.2017.05.21
10	LIU H Z , HUANG J C , GUAN Y H , et al. Accelerated degradation model of nonlinear Wiener process based on fixed time index[J]. Mathematics, 2019, 7 (5): 416- 432. doi: 10.3390/math7050416
11	WANG H W , XU T X , WANG W Y . Remaining life prediction based on Wiener processes with ADT prior information[J]. Quality and Reliability Engineering International, 2016, 32 (3): 753- 765. doi: 10.1002/qre.1788
12	WANG H , MA X B , ZHAO Y . An improved Wiener process model with adaptive drift and diffusion for online remaining useful life prediction[J]. Mechanical Systems and Signal Processing, 2019, 127, 370- 387. doi: 10.1016/j.ymssp.2019.03.019
13	孙国玺, 张清华, 文成林, 等. 基于随机退化数据建模的设备剩余寿命自适应预测方法[J]. 电子学报, 2015, 43 (6): 1119- 1126. doi: 10.3969/j.issn.0372-2112.2015.06.013
	SUN G X , ZHANG Q H , WEN C L , et al. A stochastic degradation modeling based adaptive prognostic approach for equipment[J]. Acta Electronica Sinica, 2015, 43 (6): 1119- 1126. doi: 10.3969/j.issn.0372-2112.2015.06.013
14	司小胜, 胡昌华. 数据驱动的设备剩余寿命预测理论及应用[M]. 北京: 国防工业出版社, 2016.
	SI X S , HU C H . Data-driven remaining useful life prediction theory and application for equipment[M]. Beijing: National Defense Industry Press, 2016.
15	FENG L , WANG H L , SI X S , et al. A state-space-based prognostic model for hidden and age-dependent nonlinear degradation process[J]. IEEE Trans.on Automation Science and Engineering, 2013, 10 (4): 1072- 1086. doi: 10.1109/TASE.2012.2227960
16	周源泉, 翁朝曦, 叶喜涛. 论加速系数与失效机理不变的条件(Ⅰ)——寿命型随机变量的情况[J]. 系统工程与电子技术, 1996, 18 (1): 55- 67. doi: 10.3321/j.issn:1001-506X.1996.01.008
	ZHOU Y Q , WENG Z X , YE X T . Study on accelerated factor and condition for constant failure mechanism (Ⅰ)-the case for lifetime is a random variable[J]. Systems Engineering and Electronics, 1996, 18 (1): 55- 67. doi: 10.3321/j.issn:1001-506X.1996.01.008
17	JAWINSKI A H . Stochastic processes and filtering theory[M]. New York: Academic Press, 1970.
18	王小林. 基于非线性Wiener过程的产品退化建模与剩余寿命预测研究[D]. 长沙: 国防科技大学, 2013.
	WANG X L. Research of degradation modeling and residual life estimation for deterioriate products based on nonlinear Wiener process[D]. Changsha: National University of Defense Technology, 2013.
19	WANG W , CHRISTER A H . Towards a general condition-based maintenance model for a stochastic dynamic system[J]. The Journal of the Operational Research Society, 2000, 51 (2): 145- 155. doi: 10.1057/palgrave.jors.2600863

[1]	刘鸿彬, 赵骞, 贾祥, 郭波. 失效信息已知时r/n(G)表决系统的剩余寿命预测[J]. 系统工程与电子技术, 2022, 44(5): 1738-1746.
[2]	孙世岩, 张钢, 梁伟阁, 佘博, 田福庆. 基于时间序列数据扩增和BLSTM的滚动轴承剩余寿命预测方法[J]. 系统工程与电子技术, 2022, 44(3): 1060-1068.
[3]	陈云翔, 饶益, 蔡忠义, 王泽洲. 基于改进相似性的装备部件剩余寿命预测及经济性储备策略[J]. 系统工程与电子技术, 2021, 43(9): 2688-2696.
[4]	王泽洲, 陈云翔, 蔡忠义, 项华春, 王莉莉. 基于比例关系加速退化建模的设备剩余寿命在线预测[J]. 系统工程与电子技术, 2021, 43(2): 584-592.
[5]	刘根, 王治华, 屈怀远, 李璐, 刘成瑞. 基于多变量优化的恒定应力加速退化试验设计[J]. 系统工程与电子技术, 2021, 43(1): 267-271.
[6]	李京峰, 陈云翔, 项华春, 蔡忠义. 基于LSTM-DBN的航空发动机剩余寿命预测[J]. 系统工程与电子技术, 2020, 42(7): 1637-1644.
[7]	蔡忠义, 王泽洲, 张晓丰, 李岩. 隐含非线性退化设备的剩余寿命在线预测方法[J]. 系统工程与电子技术, 2020, 42(6): 1410-1416.
[8]	张奡, 王治华, 赵振平, 秦续国, 吴琼, 刘成瑞. 两阶段非线性Wiener过程退化建模与可靠性分析[J]. 系统工程与电子技术, 2020, 42(4): 954-959.
[9]	杨志远, 赵建民, 李俐莹, 程中华, 郭驰名. 二元相关退化系统可靠性分析及剩余寿命预测[J]. 系统工程与电子技术, 2020, 42(11): 2661-2668.
[10]	丁达理, 魏政磊, 唐上钦, 黄长强. 自适应预测权重的空战鲁棒机动决策方法[J]. 系统工程与电子技术, 2020, 42(10): 2275-2284.
[11]	蔡忠义, 陈云翔, 郭建胜, 王泽洲, 邓林. 考虑测量误差和随机效应的设备剩余寿命预测#br#[J]. 系统工程与电子技术, 2019, 41(7): 1658-1664.
[12]	赵骞, 贾祥, 程志君, 郭波. 部件寿命服从威布尔分布时典型系统的寿命与剩余寿命估计[J]. 系统工程与电子技术, 2019, 41(7): 1665-1671.
[13]	王泽洲, 陈云翔, 蔡忠义, 项华春, 罗承昆. 考虑随机失效阈值的设备剩余寿命在线预测[J]. 系统工程与电子技术, 2019, 41(5): 1162-1168.
[14]	陈震, 潘浩, 潘尔顺. 恒定应力加速退化试验差异化设计[J]. 系统工程与电子技术, 2019, 41(4): 929-936.
[15]	盖炳良, 滕克难, 唐金国, 王浩伟, 孙媛. 基于马氏距离的二元退化可靠性分析[J]. 系统工程与电子技术, 2019, 41(3): 686-692.

基于比例加速退化建模的单台机载设备剩余寿命自适应预测

Adaptive prediction of remaining useful lifetime for the single airborne equipment based on the proportional accelerated degradation modeling

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 6

参考文献 19

相关文章 15

编辑推荐

Metrics

本文评价