IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v214y2021ics0951832021002714.html
   My bibliography  Save this article

An artificial neural network supported stochastic process for degradation modeling and prediction

Author

Listed:
  • Liu, Di
  • Wang, Shaoping

Abstract

An artificial neural network supported stochastic process for degradation modeling and prediction is proposed in this paper. An artificial neural network is applied to describe the degradation path due to data fitting flexibility and path description considering degradation path uncertainty. The assumption that the initial degradation is zero in the stochastic process is freed. The artificial neural network supported stochastic process is trained by minimizing the minus log-likelihood offline-based on the run-to-failure degradation data. Considering unit-to-unit variance in population degradation modeling, the process parameters are assumed to be randomly distributed. Here, three common distributions describe the process parameters, and Akaike information criteria applied to select distributions of process parameters. The process parameters are evaluated by Bayesian inference based on the trained path, and distributions of process parameters are based on real-time degradation data for online prediction. The proposed method is verified by a simulation experiment based on a Wiener process, which seemed a true model. Furthermore, an actual degradation dataset is also applied to illustrate the effectiveness of the proposed method. Both the simulation experiment and actual example indicate that the proposed stochastic process is capable of degradation modeling and degradation predicting, even without prior information about the degradation path.

Suggested Citation

  • Liu, Di & Wang, Shaoping, 2021. "An artificial neural network supported stochastic process for degradation modeling and prediction," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    • Handle: RePEc:eee:reensy:v:214:y:2021:i:c:s0951832021002714
    DOI: 10.1016/j.ress.2021.107738
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832021002714
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2021.107738?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Peng, Weiwen & Li, Yan-Feng & Mi, Jinhua & Yu, Le & Huang, Hong-Zhong, 2016. "Reliability of complex systems under dynamic conditions: A Bayesian multivariate degradation perspective," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 75-87.
    2. Lanqing Hong & Zhi-Sheng Ye & Josephine Kartika Sari, 2018. "Interval estimation for Wiener processes based on accelerated degradation test data," IISE Transactions, Taylor & Francis Journals, vol. 50(12), pages 1043-1057, December.
    3. Koosha Rafiee & Qianmei Feng & David Coit, 2014. "Reliability modeling for dependent competing failure processes with changing degradation rate," IISE Transactions, Taylor & Francis Journals, vol. 46(5), pages 483-496.
    4. Altun, Mustafa & Comert, Salih Vehbi, 2016. "A change-point based reliability prediction model using field return data," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 175-184.
    5. Peng, Weiwen & Li, Yan-Feng & Yang, Yuan-Jian & Huang, Hong-Zhong & Zuo, Ming J., 2014. "Inverse Gaussian process models for degradation analysis: A Bayesian perspective," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 175-189.
    6. Wang, Pingping & Tang, Yincai & Joo Bae, Suk & He, Yong, 2018. "Bayesian analysis of two-phase degradation data based on change-point Wiener process," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 244-256.
    7. Liu, Di & Wang, Shaoping & Zhang, Chao & Tomovic, Mileta, 2018. "Bayesian model averaging based reliability analysis method for monotonic degradation dataset based on inverse Gaussian process and Gamma process," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 25-38.
    8. Bae, Suk Joo & Yuan, Tao & Ning, Shuluo & Kuo, Way, 2015. "A Bayesian approach to modeling two-phase degradation using change-point regression," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 66-74.
    9. Nguyen, Khanh T.P. & Fouladirad, Mitra & Grall, Antoine, 2018. "Model selection for degradation modeling and prognosis with health monitoring data," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 105-116.
    10. Ma, Zhonghai & Wang, Shaoping & Ruiz, Cesar & Zhang, Chao & Liao, Haitao & Pohl, Edward, 2020. "Reliability estimation from two types of accelerated testing data considering measurement error," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    11. Jianxun Zhang & Xiao He & Xiaosheng Si & Changhua Hu & Donghua Zhou, 2017. "A Novel Multi-Phase Stochastic Model for Lithium-Ion Batteries’ Degradation with Regeneration Phenomena," Energies, MDPI, vol. 10(11), pages 1-24, October.
    12. Li, Naipeng & Gebraeel, Nagi & Lei, Yaguo & Bian, Linkan & Si, Xiaosheng, 2019. "Remaining useful life prediction of machinery under time-varying operating conditions based on a two-factor state-space model," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 88-100.
    13. Ye, Zhi-Sheng & Chen, Nan & Shen, Yan, 2015. "A new class of Wiener process models for degradation analysis," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 58-67.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tohme, Tony & Vanslette, Kevin & Youcef-Toumi, Kamal, 2023. "Reliable neural networks for regression uncertainty estimation," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    2. Yong, Weixun & Zhang, Wengang & Nguyen, Hoang & Bui, Xuan-Nam & Choi, Yosoon & Nguyen-Thoi, Trung & Zhou, Jian & Tran, Trung Tin, 2022. "Analysis and prediction of diaphragm wall deflection induced by deep braced excavations using finite element method and artificial neural network optimized by metaheuristic algorithms," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    3. Zhou, Jie & Lin, Haifei & Li, Shugang & Jin, Hongwei & Zhao, Bo & Liu, Shihao, 2023. "Leakage diagnosis and localization of the gas extraction pipeline based on SA-PSO BP neural network," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    4. Li, Yang & Xu, Jun, 2024. "Neural network-aided simulation of non-Gaussian stochastic processes," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    5. Feng, Liuyang & Zhang, Limao, 2022. "Enhanced prediction intervals of tunnel-induced settlement using the genetic algorithm and neural network," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    6. Liu, Di & Wang, Shaoping & Zhang, Chao, 2022. "Reliability estimation from two types of accelerated testing data based on an artificial neural network supported Wiener process," Applied Mathematics and Computation, Elsevier, vol. 417(C).
    7. Chen, Xingyu & Yang, Qingyu & Wu, Xin, 2022. "Nonlinear degradation model and reliability analysis by integrating image covariate," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    8. Liu, Di & Wang, Shaoping & Cui, Xiaoyu, 2022. "An artificial neural network supported Wiener process based reliability estimation method considering individual difference and measurement error," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    9. Ma, Zhonghai & Liao, Haitao & Gao, Jianhang & Nie, Songlin & Geng, Yugang, 2023. "Physics-Informed Machine Learning for Degradation Modeling of an Electro-Hydrostatic Actuator System," Reliability Engineering and System Safety, Elsevier, vol. 229(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liu, Di & Wang, Shaoping & Cui, Xiaoyu, 2022. "An artificial neural network supported Wiener process based reliability estimation method considering individual difference and measurement error," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    2. Liu, Di & Wang, Shaoping & Zhang, Chao, 2022. "Reliability estimation from two types of accelerated testing data based on an artificial neural network supported Wiener process," Applied Mathematics and Computation, Elsevier, vol. 417(C).
    3. Liu, Di & Wang, Shaoping, 2020. "A degradation modeling and reliability estimation method based on Wiener process and evidential variable," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    4. Liu, Di & Wang, Shaoping, 2021. "Reliability estimation from lifetime testing data and degradation testing data with measurement error based on evidential variable and Wiener process," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    5. Chen, Wen-Bin & Li, Xiao-Yang & Kang, Rui, 2022. "Integration for degradation analysis with multi-source ADT datasets considering dataset discrepancies and epistemic uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    6. Chen, Zhen & Li, Yaping & Zhou, Di & Xia, Tangbin & Pan, Ershun, 2021. "Two-phase degradation data analysis with change-point detection based on Gaussian process degradation model," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    7. Ma, Zhonghai & Liao, Haitao & Ji, Hui & Wang, Shaoping & Yin, Fanglong & Nie, Songlin, 2021. "Optimal design of hybrid accelerated test based on the Inverse Gaussian process model," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    8. Hu, Changhua & Xing, Yuanxing & Du, Dangbo & Si, Xiaosheng & Zhang, Jianxun, 2023. "Remaining useful life estimation for two-phase nonlinear degradation processes," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    9. Zhang, Zhengxin & Si, Xiaosheng & Hu, Changhua & Lei, Yaguo, 2018. "Degradation data analysis and remaining useful life estimation: A review on Wiener-process-based methods," European Journal of Operational Research, Elsevier, vol. 271(3), pages 775-796.
    10. Hao, Songhua & Yang, Jun & Berenguer, Christophe, 2019. "Degradation analysis based on an extended inverse Gaussian process model with skew-normal random effects and measurement errors," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 261-270.
    11. Sun, Xuxue & Mraied, Hesham & Cai, Wenjun & Zhang, Qiong & Liang, Guoyuan & Li, Mingyang, 2018. "Bayesian latent degradation performance modeling and quantification of corroding aluminum alloys," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 84-96.
    12. Gao, Hongda & Cui, Lirong & Dong, Qinglai, 2020. "Reliability modeling for a two-phase degradation system with a change point based on a Wiener process," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    13. Liao, Guobo & Yin, Hongpeng & Chen, Min & Lin, Zheng, 2021. "Remaining useful life prediction for multi-phase deteriorating process based on Wiener process," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    14. Zhang, Jian-Xun & Hu, Chang-Hua & He, Xiao & Si, Xiao-Sheng & Liu, Yang & Zhou, Dong-Hua, 2017. "Lifetime prognostics for deteriorating systems with time-varying random jumps," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 338-350.
    15. Zhengxin Zhang & Xiaosheng Si & Changhua Hu & Xiangyu Kong, 2015. "Degradation modeling–based remaining useful life estimation: A review on approaches for systems with heterogeneity," Journal of Risk and Reliability, , vol. 229(4), pages 343-355, August.
    16. Xudan Chen & Guoxun Ji & Xinli Sun & Zhen Li, 2019. "Inverse Gaussian–based model with measurement errors for degradation analysis," Journal of Risk and Reliability, , vol. 233(6), pages 1086-1098, December.
    17. Zhang, Jian-Xun & Si, Xiao-Sheng & Du, Dang-Bo & Hu, Chang-Hua & Hu, Chen, 2020. "A novel iterative approach of lifetime estimation for standby systems with deteriorating spare parts," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    18. Li, Naipeng & Gebraeel, Nagi & Lei, Yaguo & Fang, Xiaolei & Cai, Xiao & Yan, Tao, 2021. "Remaining useful life prediction based on a multi-sensor data fusion model," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    19. Wang, Xiaofei & Wang, Bing Xing & Jiang, Pei Hua & Hong, Yili, 2020. "Accurate reliability inference based on Wiener process with random effects for degradation data," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    20. Song, Wanqing & Duan, Shouwu & Zio, Enrico & Kudreyko, Aleksey, 2022. "Multifractional and long-range dependent characteristics for remaining useful life prediction of cracking gas compressor," Reliability Engineering and System Safety, Elsevier, vol. 225(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:214:y:2021:i:c:s0951832021002714. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.