IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v251y2024ics095183202400437x.html
   My bibliography  Save this article

Manufacturing system evaluation in terms of system reliability via long short-term memory

Author

Listed:
  • Huang, Cheng-Hao
  • Lin, Yi-Kuei

Abstract

With the development of system reliability evaluation, more and more studies are published by considering more attributes or adopting different calculation methods. However, existing deep learning (DL) approaches for predicting system reliability of a multi-state manufacturing network (MMN) only considering single attribute, such as time or machine failure. A more comprehensive consideration about an MMN is deliberated in this study. The Long Short-Term Memory is then utilized to construct the prediction model to process time series of an MMN data. Through the experimental results, the proposed prediction model outperforms than the existing method and other DL method in terms of root mean square error and mean absolute error. Moreover, the parameter of an MMN and hyperparameter of the proposed prediction model are discussed to investigate the optimized combination for system reliability prediction.

Suggested Citation

  • Huang, Cheng-Hao & Lin, Yi-Kuei, 2024. "Manufacturing system evaluation in terms of system reliability via long short-term memory," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    • Handle: RePEc:eee:reensy:v:251:y:2024:i:c:s095183202400437x
    DOI: 10.1016/j.ress.2024.110365
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S095183202400437X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2024.110365?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. Fang, Lei & He, Bin, 2023. "A deep learning framework using multi-feature fusion recurrent neural networks for energy consumption forecasting," Applied Energy, Elsevier, vol. 348(C).
    2. Ping-Chen Chang, 2022. "Reliability evaluation and big data analytics architecture for a stochastic flow network with time attribute," Annals of Operations Research, Springer, vol. 311(1), pages 3-18, April.
    3. Ohyung Kwon & Hyung Giun Kim & Min Ji Ham & Wonrae Kim & Gun-Hee Kim & Jae-Hyung Cho & Nam Il Kim & Kangil Kim, 2020. "A deep neural network for classification of melt-pool images in metal additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 375-386, February.
    4. Davila-Frias, Alex & Yodo, Nita & Le, Trung & Yadav, Om Prakash, 2023. "A deep neural network and Bayesian method based framework for all-terminal network reliability estimation considering degradation," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    5. Gaofeng Da & Maochao Xu & Ping Shing Chan, 2018. "An efficient algorithm for computing the signatures of systems with exchangeable components and applications," IISE Transactions, Taylor & Francis Journals, vol. 50(7), pages 584-595, July.
    6. Bai, Guanghan & Tian, Zhigang & Zuo, Ming J., 2016. "An improved algorithm for finding all minimal paths in a network," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 1-10.
    7. Yeh, Wei-Chang & Du, Chia-Ming & Tan, Shi-Yi & Forghani-elahabad, Majid, 2023. "Application of LSTM based on the BAT-MCS for binary-state network approximated time-dependent reliability problems," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    8. Yeh, Wei-Chang, 2024. "A new hybrid inequality BAT for comprehensive all-level d-MP identification using minimal paths in Multistate Flow Network reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    9. Huang, Cheng-Hao & Huang, Ding-Hsiang & Lin, Yi-Kuei, 2023. "Network reliability prediction for random capacitated-flow networks via an artificial neural network," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    10. Wang, Chu & Dou, Manfeng & Li, Zhongliang & Outbib, Rachid & Zhao, Dongdong & Zuo, Jian & Wang, Yuanlin & Liang, Bin & Wang, Peng, 2023. "Data-driven prognostics based on time-frequency analysis and symbolic recurrent neural network for fuel cells under dynamic load," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    11. Peng, Xiaosheng & Wang, Hongyu & Lang, Jianxun & Li, Wenze & Xu, Qiyou & Zhang, Zuowei & Cai, Tao & Duan, Shanxu & Liu, Fangjie & Li, Chaoshun, 2021. "EALSTM-QR: Interval wind-power prediction model based on numerical weather prediction and deep learning," Energy, Elsevier, vol. 220(C).
    12. Huang, Ding-Hsiang & Huang, Cheng-Fu & Lin, Yi-Kuei, 2020. "Exact project reliability for a multi-state project network subject to time and budget constraints," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    13. Niu, Yi-Feng & Wan, Xiao-Yu & Xu, Xiu-Zhen & Ding, Dong, 2020. "Finding all multi-state minimal paths of a multi-state flow network via feasible circulations," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    14. Lin, Yi-Kuei & Huang, Cheng-Fu & Chang, Ping-Chen, 2013. "System reliability evaluation of a touch panel manufacturing system with defect rate and reworking," Reliability Engineering and System Safety, Elsevier, vol. 118(C), pages 51-60.
    15. Li, Mingyang & Wang, Zequn, 2022. "LSTM-augmented deep networks for time-variant reliability assessment of dynamic systems," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    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. Song, Zhiting & Zhu, Jianhua & Chen, Kun, 2025. "Robustness analysis of smart manufacturing systems against resource failures: A two-layered network perspective," Reliability Engineering and System Safety, Elsevier, vol. 253(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. Xu, Bei & Liu, Tao & Bai, Guanghan & Tao, Junyong & Zhang, Yun-an & Fang, Yining, 2022. "A multistate network approach for reliability evaluation of unmanned swarms by considering information exchange capacity," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    2. Forghani-elahabad, Majid & Yeh, Wei-Chang, 2022. "An improved algorithm for reliability evaluation of flow networks," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    3. Da, Gaofeng & Zhang, Xin & He, Zhenwen & Ding, Weiyong, 2025. "Estimating the all-terminal signatures for networks by using deep neural network," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    4. Niu, Yi-Feng & Wang, Jun-Feng & Xu, Xiu-Zhen & Xu, Qian-Xin, 2025. "Reliability evaluation for a multi-commodity multi-state distribution network under transportation emission consideration," Reliability Engineering and System Safety, Elsevier, vol. 254(PA).
    5. Li, Shunlong & Wang, Jie & He, Shaoyang, 2023. "Connectivity probability evaluation of a large-scale highway bridge network using network decomposition," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    6. Esha Datta & Neeraj Goyal, 2023. "An efficient sum of disjoint product method for reliability evaluation of stochastic flow networks using d-MPs," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(4), pages 1228-1246, August.
    7. Thi-Phuong Nguyen, 2021. "Assess the Impacts of Discount Policies on the Reliability of a Stochastic Air Transport Network," Mathematics, MDPI, vol. 9(9), pages 1-13, April.
    8. Yanghe Liu & Hairong Zhang & Chuanfeng Wu & Mengxin Shao & Liting Zhou & Wenlong Fu, 2024. "A Short-Term Wind Speed Forecasting Framework Coupling a Maximum Information Coefficient, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Shared Weight Gated Memory Network with Im," Sustainability, MDPI, vol. 16(16), pages 1-19, August.
    9. Chang, Ping-Chen, 2024. "A path-based simulation approach for multistate flow network reliability estimation without using boundary points," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    10. Zuo, Jian & Cadet, Catherine & Li, Zhongliang & Bérenguer, Christophe & Outbib, Rachid, 2024. "A deterioration-aware energy management strategy for the lifetime improvement of a multi-stack fuel cell system subject to a random dynamic load," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    11. Ye, Xiaoling & Liu, Chengcheng & Xiong, Xiong & Qi, Yinyi, 2025. "Recurrent attention encoder–decoder network for multi-step interval wind power prediction," Energy, Elsevier, vol. 315(C).
    12. Yu-Chung Tsao & Thuy-Linh Vu, 2023. "Electricity pricing, capacity, and predictive maintenance considering reliability," Annals of Operations Research, Springer, vol. 322(2), pages 991-1011, March.
    13. Bai, Guo-Peng & Er, Guo-Kang & Iu, Vai Pan, 2024. "A novel stochastic approach to investigate the probabilistic characteristics of the ship roll system with sinusoidal restoring force," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    14. Haritha, P.C. & Anjaneyulu, M.V.L.R., 2024. "Comparison of topological functionality-based resilience metrics using link criticality," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    15. Ding, Jiaqi & Zhao, Pu & Liu, Changjun & Wang, Xiaofang & Xie, Rong & Liu, Haitao, 2024. "From irregular to continuous: The deep Koopman model for time series forecasting of energy equipment," Applied Energy, Elsevier, vol. 364(C).
    16. Brian Godwin Lim & Renzo Roel Tan & Richard de Jesus & Lessandro Estelito Garciano & Agnes Garciano & Kazushi Ikeda, 2025. "Path survival reliabilities as measures of reliability for lifeline utility networks," Journal of Combinatorial Optimization, Springer, vol. 49(4), pages 1-24, May.
    17. Khan, Zulfiqar Ahmad & Khan, Shabbir Ahmad & Hussain, Tanveer & Baik, Sung Wook, 2024. "DSPM: Dual sequence prediction model for efficient energy management in micro-grid," Applied Energy, Elsevier, vol. 356(C).
    18. Niu, Yi-Feng & Xiang, Hai-Yan & Xu, Xiu-Zhen, 2024. "Expected performance evaluation and optimization of a multi-distribution multi-state logistics network based on network reliability," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    19. Yang, Ting & Yang, Zhenning & Li, Fei & Wang, Hengyu, 2024. "A short-term wind power forecasting method based on multivariate signal decomposition and variable selection," Applied Energy, Elsevier, vol. 360(C).
    20. Geng, Sunyue & Liu, Sifeng & Fang, Zhigeng, 2021. "Resilient communication model for satellite networks using clustering technique," Reliability Engineering and System Safety, Elsevier, vol. 215(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:251:y:2024:i:c:s095183202400437x. 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.