IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i12p2066-d839204.html
   My bibliography  Save this article

LSTM-Based Broad Learning System for Remaining Useful Life Prediction

Author

Listed:
  • Xiaojia Wang

    (School of Management, Hefei University of Technology, Hefei 230009, China)

  • Ting Huang

    (School of Management, Hefei University of Technology, Hefei 230009, China)

  • Keyu Zhu

    (School of Management, Hefei University of Technology, Hefei 230009, China)

  • Xibin Zhao

    (School of Software, Tsinghua University, Beijing 100084, China)

Abstract

Prognostics and health management (PHM) are gradually being applied to production management processes as industrial production is gradually undergoing a transformation, turning into intelligent production and leading to increased demands on the reliability of industrial equipment. Remaining useful life (RUL) prediction plays a pivotal role in this process. Accurate prediction results can effectively provide information about the condition of the equipment on which intelligent maintenance can be based, with many methods applied to this task. However, the current problems of inadequate feature extraction and poor correlation between prediction results and data still affect the prediction accuracy. To overcome these obstacles, we constructed a new fusion model that extracts data features based on a broad learning system (BLS) and embeds long short-term memory (LSTM) to process time-series information, named as the B-LSTM. First, the LSTM controls the transmission of information from the data to the gate mechanism, and the retained information generates the mapped features and forms the feature nodes. Then, the random feature nodes are supplemented by an activation function that generates enhancement nodes with greater expressive power, increasing the nonlinear factor in the network, and eventually the feature nodes and enhancement nodes are jointly connected to the output layer. The B-LSTM was experimentally used with the C-MAPSS dataset and the results of comparison with several mainstream methods showed that the new model achieved significant improvements.

Suggested Citation

  • Xiaojia Wang & Ting Huang & Keyu Zhu & Xibin Zhao, 2022. "LSTM-Based Broad Learning System for Remaining Useful Life Prediction," Mathematics, MDPI, vol. 10(12), pages 1-13, June.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:12:p:2066-:d:839204
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/12/2066/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/12/2066/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yongbin Liu & Bing He & Fang Liu & Siliang Lu & Yilei Zhao & Jiwen Zhao, 2016. "Remaining Useful Life Prediction of Rolling Bearings Using PSR, JADE, and Extreme Learning Machine," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-13, April.
    2. van Noortwijk, J.M. & van der Weide, J.A.M. & Kallen, M.J. & Pandey, M.D., 2007. "Gamma processes and peaks-over-threshold distributions for time-dependent reliability," Reliability Engineering and System Safety, Elsevier, vol. 92(12), pages 1651-1658.
    3. Zio, Enrico & Di Maio, Francesco, 2010. "A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system," Reliability Engineering and System Safety, Elsevier, vol. 95(1), pages 49-57.
    4. Li, Xiang & Ding, Qian & Sun, Jian-Qiao, 2018. "Remaining useful life estimation in prognostics using deep convolution neural networks," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 1-11.
    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. Cui, Zhenhua & Kang, Le & Li, Liwei & Wang, Licheng & Wang, Kai, 2022. "A combined state-of-charge estimation method for lithium-ion battery using an improved BGRU network and UKF," Energy, Elsevier, vol. 259(C).
    2. Roland Bolboacă & Piroska Haller, 2023. "Performance Analysis of Long Short-Term Memory Predictive Neural Networks on Time Series Data," Mathematics, MDPI, vol. 11(6), pages 1-35, March.
    3. Ning Ma & Huaixian Yin & Kai Wang, 2023. "Prediction of the Remaining Useful Life of Supercapacitors at Different Temperatures Based on Improved Long Short-Term Memory," Energies, MDPI, vol. 16(14), pages 1-14, July.
    4. Xinwei Sun & Yang Zhang & Yongcheng Zhang & Licheng Wang & Kai Wang, 2023. "Summary of Health-State Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy," Energies, MDPI, vol. 16(15), pages 1-19, July.
    5. Dezhi Li & Dongfang Yang & Liwei Li & Licheng Wang & Kai Wang, 2022. "Electrochemical Impedance Spectroscopy Based on the State of Health Estimation for Lithium-Ion Batteries," Energies, MDPI, vol. 15(18), pages 1-26, September.
    6. Cui, Zhenhua & Kang, Le & Li, Liwei & Wang, Licheng & Wang, Kai, 2022. "A hybrid neural network model with improved input for state of charge estimation of lithium-ion battery at low temperatures," Renewable Energy, Elsevier, vol. 198(C), pages 1328-1340.
    7. Shengyang Lu & Yu Zhu & Lihu Dong & Guangyu Na & Yan Hao & Guanfeng Zhang & Wuyang Zhang & Shanshan Cheng & Junyou Yang & Yuqiu Sui, 2022. "Small-Signal Stability Research of Grid-Connected Virtual Synchronous Generators," Energies, MDPI, vol. 15(19), pages 1-17, September.

    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. Costa, Nahuel & Sánchez, Luciano, 2022. "Variational encoding approach for interpretable assessment of remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    2. Yuanju Qu & Zengtao Hou, 2022. "Degradation principle of machines influenced by maintenance," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1521-1530, June.
    3. da Costa, Paulo Roberto de Oliveira & Akçay, Alp & Zhang, Yingqian & Kaymak, Uzay, 2020. "Remaining useful lifetime prediction via deep domain adaptation," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    4. Pan, Zhengqiang & Balakrishnan, Narayanaswamy, 2011. "Reliability modeling of degradation of products with multiple performance characteristics based on gamma processes," Reliability Engineering and System Safety, Elsevier, vol. 96(8), pages 949-957.
    5. Chang, Miaoxin & Huang, Xianzhen & Coolen, Frank P.A. & Coolen-Maturi, Tahani, 2021. "Reliability analysis for systems based on degradation rates and hard failure thresholds changing with degradation levels," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    6. Lin, Yan-Hui & Chang, Liang & Guan, Lu-Xin, 2024. "Enhanced stochastic recurrent hybrid model for RUL Predictions via Semi-supervised learning," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    7. Hui Chen & Jie Chen & Yangyang Lai & Xiaoqi Yu & Lijun Shang & Rui Peng & Baoliang Liu, 2024. "Discrete Random Renewable Replacements after the Expiration of Collaborative Preventive Maintenance Warranty," Mathematics, MDPI, vol. 12(18), pages 1-21, September.
    8. Li, Yuanfu & Chen, Yifan & Shao, Haonan & Zhang, Huisheng, 2023. "A novel dual attention mechanism combined with knowledge for remaining useful life prediction based on gated recurrent units," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    9. Xiao, Lei & Tang, Junxuan & Zhang, Xinghui & Bechhoefer, Eric & Ding, Siyi, 2021. "Remaining useful life prediction based on intentional noise injection and feature reconstruction," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    10. Li, Yuanfu & Chen, Yao & Hu, Zhenchao & Zhang, Huisheng, 2023. "Remaining useful life prediction of aero-engine enabled by fusing knowledge and deep learning models," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    11. Marcin Witczak & Marcin Mrugalski & Bogdan Lipiec, 2021. "Remaining Useful Life Prediction of MOSFETs via the Takagi–Sugeno Framework," Energies, MDPI, vol. 14(8), pages 1-23, April.
    12. Liu, Lu & Song, Xiao & Zhou, Zhetao, 2022. "Aircraft engine remaining useful life estimation via a double attention-based data-driven architecture," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    13. Zhang, Jiusi & Li, Xiang & Tian, Jilun & Luo, Hao & Yin, Shen, 2023. "An integrated multi-head dual sparse self-attention network for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    14. Yaping Li & Enrico Zio & Ershun Pan, 2021. "An MEWMA-based segmental multivariate hidden Markov model for degradation assessment and prediction," Journal of Risk and Reliability, , vol. 235(5), pages 831-844, October.
    15. Zhuang, Jichao & Jia, Minping & Ding, Yifei & Ding, Peng, 2021. "Temporal convolution-based transferable cross-domain adaptation approach for remaining useful life estimation under variable failure behaviors," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    16. García Nieto, P.J. & García-Gonzalo, E. & Sánchez Lasheras, F. & de Cos Juez, F.J., 2015. "Hybrid PSO–SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 219-231.
    17. Khaled Akkad & David He, 2023. "A dynamic mode decomposition based deep learning technique for prognostics," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2207-2224, June.
    18. Nadire Cavus & Yakubu Bala Mohammed & Mohammed Nasiru Yakubu, 2021. "An Artificial Intelligence-Based Model for Prediction of Parameters Affecting Sustainable Growth of Mobile Banking Apps," Sustainability, MDPI, vol. 13(11), pages 1-21, May.
    19. Le Son, Khanh & Fouladirad, Mitra & Barros, Anne & Levrat, Eric & Iung, Benoît, 2013. "Remaining useful life estimation based on stochastic deterioration models: A comparative study," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 165-175.
    20. Zhuang, Liangliang & Xu, Ancha & Wang, Xiao-Lin, 2023. "A prognostic driven predictive maintenance framework based on Bayesian deep learning," Reliability Engineering and System Safety, Elsevier, vol. 234(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:gam:jmathe:v:10:y:2022:i:12:p:2066-:d:839204. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.