IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v214y2021ics0951832021003306.html
   My bibliography  Save this article

Prediction of remaining useful life of multi-stage aero-engine based on clustering and LSTM fusion

Author

Listed:
  • Liu, Junqiang
  • Lei, Fan
  • Pan, Chunlu
  • Hu, Dongbin
  • Zuo, Hongfu

Abstract

Accurately predicting the Remaining Useful Life (RUL) of an aero-engine is of great significance for airlines to make maintenance plans reasonably and reduce maintenance costs effectively. Traditional single-parameter and single-stage models achieve low prediction accuracy. In order to improve the prediction accuracy of the RUL of the aero-engine, a novel aero-engine RUL prediction model named Improved multi-stage Long Short Term Memory network with Clustering (ILSTMC) is proposed. Based on this model, we research a corresponding multi-stage RUL prediction algorithm, which integrates the advantages of clustering analysis and LSTM model. The National Aeronautics and Space Administration (NASA) dataset is adopted for verification. The experimental results show that the method provided in this paper reduces the prediction error of the aero-engine RUL effectively. In the cases of multi-stage prediction, the prediction error of ILSTMC is the smallest compared with LSTM, Recurrent Neural Networks (RNN) and Linear Programming (LP) methods. In the multi-stage prediction of RUL, it is evaluated adopting Root Mean Squared Error (RMSE) and prediction error. The RMSE of the last stage is reduced by 0.85% compared to LSTM, the RMSE of each stage is reduced by 1.87% compared to LSTM on average; the accuracy of life time cycle is better than LSTM by 0.59%, and the average accuracy of life time cycle at each stage is improved by 1.84% compared to LSTM. The results reveal that the proposed ILSTMC model effectively improves the prediction accuracy of RUL.

Suggested Citation

  • Liu, Junqiang & Lei, Fan & Pan, Chunlu & Hu, Dongbin & Zuo, Hongfu, 2021. "Prediction of remaining useful life of multi-stage aero-engine based on clustering and LSTM fusion," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
  • Handle: RePEc:eee:reensy:v:214:y:2021:i:c:s0951832021003306
    DOI: 10.1016/j.ress.2021.107807
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832021003306
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2021.107807?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. Fan, Dongyan & Sun, Hai & Yao, Jun & Zhang, Kai & Yan, Xia & Sun, Zhixue, 2021. "Well production forecasting based on ARIMA-LSTM model considering manual operations," Energy, Elsevier, vol. 220(C).
    2. Moghaddass, Ramin & Zuo, Ming J., 2014. "An integrated framework for online diagnostic and prognostic health monitoring using a multistate deterioration process," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 92-104.
    3. Yu, Wennian & Kim, II Yong & Mechefske, Chris, 2020. "An improved similarity-based prognostic algorithm for RUL estimation using an RNN autoencoder scheme," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    4. Prabhakar V. Varde & Michael G. Pecht, 2018. "Prognostics and Health Management," Springer Series in Reliability Engineering, in: Risk-Based Engineering, chapter 0, pages 447-507, Springer.
    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. Xu, Danyang & Qiu, Haobo & Gao, Liang & Yang, Zan & Wang, Dapeng, 2022. "A novel dual-stream self-attention neural network for remaining useful life estimation of mechanical systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    2. Zhu, Yongmeng & Wu, Jiechang & Wu, Jun & Liu, Shuyong, 2022. "Dimensionality reduce-based for remaining useful life prediction of machining tools with multisensor fusion," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    3. Cui, Zhiquan & Yan, Zhiqi & Zhao, Minghang & Zhong, Shisheng, 2022. "Gas path parameter prediction of aero-engine based on an autoregressive discrete convolution sum process neural network," Chaos, Solitons & Fractals, Elsevier, vol. 154(C).
    4. Zhang, Huixin & Xi, Xiaopeng & Pan, Rong, 2023. "A two-stage data-driven approach to remaining useful life prediction via long short-term memory networks," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    5. 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).
    6. Wan, Shaoke & Li, Xiaohu & Zhang, Yanfei & Liu, Shijie & Hong, Jun & Wang, Dongfeng, 2022. "Bearing remaining useful life prediction with convolutional long short-term memory fusion networks," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    7. Guishuang Tian & Shaoping Wang & Jian Shi & Yajing Qiao, 2022. "State Estimation and Remaining Useful Life Prediction of PMSTM Based on a Combination of SIR and HSMM," Sustainability, MDPI, vol. 14(24), pages 1-21, December.
    8. Xu, Dan & Xiao, Xiaoqi & Liu, Jie & Sui, Shaobo, 2023. "Spatio-temporal degradation modeling and remaining useful life prediction under multiple operating conditions based on attention mechanism and deep learning," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    9. 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).
    10. Panjapornpon, Chanin & Bardeeniz, Santi & Hussain, Mohamed Azlan, 2023. "Deep learning approach for energy efficiency prediction with signal monitoring reliability for a vinyl chloride monomer process," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    11. Xiao, Dasheng & Lin, Zhifu & Yu, Aiyang & Tang, Ke & Xiao, Hong, 2024. "Data-driven method embedded physical knowledge for entire lifecycle degradation monitoring in aircraft engines," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    12. 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).
    13. 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).
    14. Zhang, Jiusi & Jiang, Yuchen & Wu, Shimeng & Li, Xiang & Luo, Hao & Yin, Shen, 2022. "Prediction of remaining useful life based on bidirectional gated recurrent unit with temporal self-attention mechanism," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    15. Zhou, Liang & Wang, Huawei & Xu, Shanshan, 2023. "Aero-engine prognosis strategy based on multi-scale feature fusion and multi-task parallel learning," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    16. Yang, Jing & Wang, Xiaomin, 2024. "Meta-learning with deep flow kernel network for few shot cross-domain remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    17. Huang, Zhifu & Yang, Yang & Hu, Yawei & Ding, Xiang & Li, Xuanlin & Liu, Yongbin, 2023. "Attention-augmented recalibrated and compensatory network for machine remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    18. Chen, Jiaxian & Li, Dongpeng & Huang, Ruyi & Chen, Zhuyun & Li, Weihua, 2023. "Aero-engine remaining useful life prediction method with self-adaptive multimodal data fusion and cluster-ensemble transfer regression," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    19. He, Yuxuan & Su, Huai & Zio, Enrico & Peng, Shiliang & Fan, Lin & Yang, Zhaoming & Yang, Zhe & Zhang, Jinjun, 2023. "A systematic method of remaining useful life estimation based on physics-informed graph neural networks with multisensor data," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    20. Xiang, Sheng & Qin, Yi & Luo, Jun & Pu, Huayan & Tang, Baoping, 2021. "Multicellular LSTM-based deep learning model for aero-engine remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    21. Zhou, Hang & Farsi, Maryam & Harrison, Andrew & Parlikad, Ajith Kumar & Brintrup, Alexandra, 2023. "Civil aircraft engine operation life resilient monitoring via usage trajectory mapping on the reliability contour," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    22. Liu, Junqiang & Yu, Zhuoqian & Zuo, Hongfu & Fu, Rongchunxue & Feng, Xiaonan, 2022. "Multi-stage residual life prediction of aero-engine based on real-time clustering and combined prediction model," Reliability Engineering and System Safety, Elsevier, vol. 225(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. Wang, Delu & Gan, Jun & Mao, Jinqi & Chen, Fan & Yu, Lan, 2023. "Forecasting power demand in China with a CNN-LSTM model including multimodal information," Energy, Elsevier, vol. 263(PE).
    2. Kamei, Sayaka & Taghipour, Sharareh, 2023. "A comparison study of centralized and decentralized federated learning approaches utilizing the transformer architecture for estimating remaining useful life," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    3. Chen, Xiaowu & Liu, Zhen, 2022. "A long short-term memory neural network based Wiener process model for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    4. 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).
    5. Lewis, Austin D. & Groth, Katrina M., 2022. "Metrics for evaluating the performance of complex engineering system health monitoring models," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    6. Coraça, Eduardo M. & Ferreira, Janito V. & Nóbrega, Eurípedes G.O., 2023. "An unsupervised structural health monitoring framework based on Variational Autoencoders and Hidden Markov Models," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    7. Fan, Linchuan & Chai, Yi & Chen, Xiaolong, 2022. "Trend attention fully convolutional network for remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    8. Ying Liao & Yisha Xiang & Min Wang, 2021. "Health assessment and prognostics based on higher‐order hidden semi‐Markov models," Naval Research Logistics (NRL), John Wiley & Sons, vol. 68(2), pages 259-276, March.
    9. Anna Samnioti & Vassilis Gaganis, 2023. "Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part II," Energies, MDPI, vol. 16(18), pages 1-53, September.
    10. Wen, Kai & Jiao, Jianfeng & Zhao, Kang & Yin, Xiong & Liu, Yuan & Gong, Jing & Li, Cuicui & Hong, Bingyuan, 2023. "Rapid transient operation control method of natural gas pipeline networks based on user demand prediction," Energy, Elsevier, vol. 264(C).
    11. Lv, Sheng-Xiang & Wang, Lin, 2022. "Deep learning combined wind speed forecasting with hybrid time series decomposition and multi-objective parameter optimization," Applied Energy, Elsevier, vol. 311(C).
    12. Kumar, Anil & Parkash, Chander & Vashishtha, Govind & Tang, Hesheng & Kundu, Pradeep & Xiang, Jiawei, 2022. "State-space modeling and novel entropy-based health indicator for dynamic degradation monitoring of rolling element bearing," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    13. Hai Wang & Shengnan Chen, 2023. "Insights into the Application of Machine Learning in Reservoir Engineering: Current Developments and Future Trends," Energies, MDPI, vol. 16(3), pages 1-11, January.
    14. Moradi, Ramin & Cofre-Martel, Sergio & Lopez Droguett, Enrique & Modarres, Mohammad & Groth, Katrina M., 2022. "Integration of deep learning and Bayesian networks for condition and operation risk monitoring of complex engineering systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    15. Fang, Xiaolei & Paynabar, Kamran & Gebraeel, Nagi, 2017. "Multistream sensor fusion-based prognostics model for systems with single failure modes," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 322-331.
    16. Prashant Kumar & Salman Khalid & Heung Soo Kim, 2023. "Prognostics and Health Management of Rotating Machinery of Industrial Robot with Deep Learning Applications—A Review," Mathematics, MDPI, vol. 11(13), pages 1-37, July.
    17. Deep, Akash & Zhou, Shiyu & Veeramani, Dharmaraj & Chen, Yong, 2023. "Partially observable Markov decision process-based optimal maintenance planning with time-dependent observations," European Journal of Operational Research, Elsevier, vol. 311(2), pages 533-544.
    18. Soheila Khajoui & Saeid Dehyadegari & Sayyed Abdolmajid Jalaee, 2024. "Forecasting Imports in OECD Member Countries and Iran by Using Neural Network Algorithms of LSTM," Papers 2402.01648, arXiv.org.
    19. Wei, Yupeng & Wu, Dazhong & Terpenny, Janis, 2021. "Learning the health index of complex systems using dynamic conditional variational autoencoders," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    20. Hundi, Prabhas & Shahsavari, Rouzbeh, 2020. "Comparative studies among machine learning models for performance estimation and health monitoring of thermal power plants," Applied Energy, Elsevier, vol. 265(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:s0951832021003306. 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.