|
|
|
| Individual Residual Life Prediction Method of Electronic Components Based on Model Fusion |
| Zhao Changdong1,2, Xiang Shihu1,2, Wang Yao1,2 |
1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin 300130 China;
2. Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province Hebei University of Technology Tianjin 300130 China |
|
|
|
Abstract Electronic components are usually the weak parts of a system and are generally required to fulfill critical tasks. If a seriously degraded electronic component is not replaced in time, its failure may result in the failure of the whole system. Therefore, it is significant to accurately predict the residual life of electronic components in the system. Due to factors such as complex failure mechanisms, little prior information, and insufficient monitoring data, the degradation model of an electronic component is usually uncertain, which may cause an inaccurate prediction of the residual life. However, the existing studies did not consider the predictive ability of a model and required much prior information or degradation data. Therefore, a residual life prediction method for an individual electronic component based on model fusion is proposed in this paper. Alternative models are fused based on a novel model evaluation index that incorporates the fitting ability, complexity, and prediction accuracy of a model. Then the residual life of an electronic component is predicted based on the fused model.
The proposed residual life prediction method is focused on the case that a single performance parameter can characterize the performance of an electronic component. Firstly, the alternative degradation model is established for typical and common degradation patterns of electronic components. Secondly, the alternative residual life distribution model is derived based on the alternative degradation model. Thirdly, the maximum likelihood estimation method is adopted to estimate the unknown parameters in the alternative model using the available degradation data collected up to the current time. Fourthly, the probability that an alternative model is the true one is constructed according to the proposed novel model evaluation index. Then the alternative residual life distribution models are fused via the law of total probability to obtain the fused model of the residual life distribution. Fifthly, the expected residual life is taken as the point estimate of the residual life and calculated based on the fused residual life distribution model.
Three real cases are carried out to verify the proposed residual life prediction method. Through comparison analysis, it is shown that the proposed method is superior to the methods that predict the residual life based on the best model selected according to a traditional model evaluation index, such as the Akaike information criterion (AIC) and the value of the likelihood function, or the proposed index. Moreover, it is verified that the proposed method can ensure good prediction accuracy, even if the regularity of the degradation data is poor and the amount of the data is small.
Some specific conclusions are summarized as follows. (1) The proposed novel model evaluation index is comprehensive since it simultaneously considers the fitting ability, complexity, and prediction accuracy of a model. (2) The strategy of model fusion can reduce the risk of abandoning a model with better prediction results and make a fused model have the characteristics of all the alternative models. The drawbacks of using a single model can be overcome to a certain extent. (3) The proposed residual life prediction method for electronic components is independent of much prior information or degradation data and has a high prediction accuracy and powerful practicability.
|
|
Received: 16 July 2022
|
|
|
|
|
|
[1] 周荔丹, 闫朝鑫, 姚钢, 等. 空间辐射环境对航天器分布式电力系统关键部件的影响及应对策略[J]. 电工技术学报, 2022, 37(6): 1365-1380.
Zhou Lidan, Yan Chaoxin, Yao Gang, et al.Influence of space radiation environment on critical components of spacecraft distributed power system and counter-measures[J]. Transactions of China Electrotechnical Society, 2022, 37(6): 1365-1380.
[2] 韩乔妮, 姜帆, 程泽. 变温度下IHF-IGPR框架的锂离子电池健康状态预测方法[J]. 电工技术学报, 2021, 36(17): 3705-3720.
Han Qiaoni, Jiang Fan, Cheng Ze.State of health estimation for lithium-ion batteries based on the framework of IHF-IGPR under variable tempe-rature[J]. Transactions of China Electrotechnical Society, 2021, 36(17): 3705-3720.
[3] 李辉, 刘人宽, 王晓, 等. 压接型IGBT器件封装退化监测方法综述[J]. 电工技术学报, 2021, 36(12): 2505-2521.
Li Hui, Liu Renkuan, Wang Xiao, et al.Review on package degradation monitoring methods of press-pack IGBT modules[J]. Transactions of China Elec-trotechnical Society, 2021, 36(12): 2505-2521.
[4] 郭劲言. 模型不确定的电主轴加速退化试验多目标优化设计方法[D]. 长春: 吉林大学, 2021.
[5] Jiao Jian, De Xinlin, Chen Zhiwei, et al.Integrated circuit failure analysis and reliability prediction based on physics of failure[J]. Engineering Failure Analysis, 2019, 104: 714-726.
[6] Zhou Yuege, Ye Xuerong, Zhai Guofu.Degradation model and maintenance strategy of the electrolytic capacitors for electronics applications[C]//2011 Pro-gnostics and System Health Managment Confernece, Shenzhen, China, 2011: 1-6.
[7] Celaya J R, Kulkarni C, Saha S, et al.Accelerated aging in electrolytic capacitors for prognostics[C]//2012 Proceedings Annual Reliability and Maintaina-bility Symposium, Reno, NV, USA, 2012: 1-6.
[8] 孟晓凯, 王志强, 李国锋. 基于硬度保留率的船用电缆剩余寿命快速评估[J]. 电工技术学报, 2016, 31(15): 197-203.
Meng Xiaokai, Wang Zhiqiang, Li Guofeng.Rapid assessment of marine cable remaining life based on retention rate of hardness[J]. Transactions of China Electrotechnical Society, 2016, 31(15): 197-203.
[9] Lin Yanhui, Ding Zeqi.An integrated degradation modeling framework considering model uncertainty and calibration[J]. Mechanical Systems and Signal Processing, 2022, 166: 108389.
[10] Yan Bingxin, Ma Xiaobing, Wang Han.Analysis of an improved Wiener deterioration model considering mechanism equivalence[J]. Journal of Physics: Con-ference Series, 2018, 1053: 012115.
[11] Wang Lizhi, Pan Rong, Li Xiaoyang, et al.A Bayesian reliability evaluation method with integrated accelerated degradation testing and field infor-mation[J]. Reliability Engineering & System Safety, 2013, 112: 38-47.
[12] 王浩伟, 徐廷学, 刘勇. 基于随机参数Gamma过程的剩余寿命预测方法[J]. 浙江大学学报(工学版), 2015, 49(4): 699-704, 762.
Wang Haowei, Xu Tingxue, Liu Yong.Remaining useful life prediction method based on Gamma processes with random parameters[J]. Journal of Zhejiang University (Engineering Science), 2015, 49(4): 699-704, 762.
[13] Yu Yong, Hu Changhua, Si Xiaosheng, et al.Modified Bayesian D-optimality for accelerated degradation test design with model uncertainty[J]. IEEE Access, 2019, 7: 42181-42189.
[14] Liu Le, Li Xiaoyang, Jiang Tongmin, et al.Optimal design for accelerated degradation tests with stochastic model uncertainty[M]. Risk, Reliability and Safety: Innovating Theory and Practice. Taylor & Francis Group, 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742: CRC Press, 2016: 10-15.
[15] Kang Rui, Gong Wenjun, Chen Yunxia.Model-driven degradation modeling approaches: investigation and review[J]. Chinese Journal of Aeronautics, 2020, 33(4): 1137-1153.
[16] Schössler T, Schön F, Lemier C, et al.Reliability improvements of thin film platinum resistors on wafer-level and micro-hotplates at stress temperatures in the range of 140-290℃[J]. Microelectronics Reliability, 2020, 104: 113557.
[17] 张月, 卓青青, 刘红侠, 等. 功率MOSFET的负偏置温度不稳定性效应中的平衡现象[J]. 物理学报, 2013, 62(16): 167305.
Zhang Yue, Zhuo Qingqing, Liu Hongxia, et al.Flat-roof of dynamic equilibrium phenomenon in static negative biase temperature instability effect on power metal-oxide-semiconductor field-effect transi-stor[J]. Acta Physica Sinica, 2013, 62(16): 167305.
[18] Whitmore G A, Schenkelberg F.Modelling acceler-ated degradation data using Wiener diffusion with a time scale transformation[J]. Lifetime Data Analysis, 1997, 3(1): 27-45.
[19] Liu Le, Li Xiaoyang, Zio E, et al.Model uncertainty in accelerated degradation testing analysis[J]. IEEE Transactions on Reliability, 2017, 66(3): 603-615.
[20] 潘广泽, 张铮, 罗琴, 等. 基于性能退化的电力电子器件寿命评估[J]. 中国电力, 2020, 53(10): 156-162.
Pan Guangze, Zhang Zheng, Luo Qin, et al.Per-formance degradation based lifetime evaluation of power electronic devices[J]. Electric Power, 2020, 53(10): 156-162.
[21] Zhou Rensheng, Serban N, Gebraeel N.Degradation-based residual life prediction under different envi-ronments[J]. Annals of Applied Statistics, 2014, 8(3): 1671-1689.
[22] Ye Zhisheng, Xie Min.Stochastic modelling and analysis of degradation for highly reliable products[J]. Applied Stochastic Models in Business and Industry, 2015, 31: 16-32.
[23] 李奎, 高志成, 武一, 等. 基于统计回归和非线性Wiener过程的交流接触器剩余寿命预测[J]. 电工技术学报, 2019, 34(19): 4058-4070.
Li Kui, Gao Zhicheng, Wu Yi, et al.Remaining lifetime prediction of AC contactor based on statistical regression and nonlinear Wiener process[J]. Transactions of China Electrotechnical Society, 2019, 34(19): 4058-4070.
[24] 刘帼巾, 李想, 王泽, 等. 基于Wiener过程电子式漏电断路器的剩余寿命预测[J]. 电工技术学报, 2022, 37(2): 528-536.
Liu Guojin, Li Xiang, Wang Ze, et al.Remaining life prediction of electronic residual current circuit breaker based on Wiener process[J]. Transactions of China Electrotechnical Society, 2022, 37(2): 528-536.
[25] Byrd R H, Gilbert J C, Nocedal J.A trust region method based on interior point techniques for nonlinear programming[J]. Mathematical Program-ming, 2000, 89(1): 149-185.
[26] Liu Baoding.Uncertainty theory[M]. Berlin: Springer, 2010.
[27] 何志鹏, 赵虎. 微型断路器电寿命评估[J]. 电工技术学报, 2022, 37(4): 1031-1040.
He Zhipeng, Zhao Hu.Electrical lifespan evaluation of miniature circuit breakers[J]. Transactions of China Electrotechnical Society, 2022, 37(4): 1031-1040.
[28] Coello C A.A comprehensive survey of evolutionary-based multiobjective optimization techniques[J]. Knowledge and Information Systems, 1999, 1(3): 269-308.
[29] 夏冬, 吴俊勇, 贺电, 等. 一种新型的风电功率预测综合模型[J]. 电工技术学报, 2011, 26(增刊1): 262-266.
Xia Dong, Wu Junyong, He Dian, et al.A novel combined model for wind power forecasting based on maximum entropy principle[J]. Transactions of China Electrotechnical Society, 2011, 26(S1): 262-266.
[30] Campagnolo L Q, Celes W, de Figueiredo L H. Accurate volume rendering based on adaptive nume-rical integration[C]//2015 28th SIBGRAPI Confer-ence on Graphics, Patterns and Images, Salvador, Brazil, 2015: 17-24.
[31] Cadini F, Sbarufatti C, Cancelliere F, et al.State-of-life prognosis and diagnosis of lithium-ion batteries by data-driven particle filters[J]. Applied Energy, 2019, 235: 661-672.
[32] Nelson W, Meeker W Q, Escobar L A.Statistical methods for reliability data[J]. Technometrics, 1998, 40(3): 255.
[33] 王浩伟. 加速退化数据建模与统计分析方法及工程应用[M]. 北京: 科学出版社, 2019.
[34] Park C, Padgett W J.New cumulative damage models for failure using stochastic processes as initial damage[J]. IEEE Transactions on Reliability, 2005, 54(3): 530-540.
[35] Park C, Padgett W J.Stochastic degradation models with several accelerating variables[J]. IEEE Transa-ctions on Reliability, 2006, 55(2): 379-390.
[36] Li Xiaoyang, Chen Dayu, Wu Jipeng, et al.3-Dimensional general ADT modeling and analysis: considering epistemic uncertainties in unit, time and stress dimension[J]. Reliability Engineering & System Safety, 2022, 225: 108577. |
|
|
|
2023, Vol. 38 
