IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v215y2021ics0951832021003902.html
   My bibliography  Save this article

Remaining useful life prediction based on intentional noise injection and feature reconstruction

Author

Listed:
  • Xiao, Lei
  • Tang, Junxuan
  • Zhang, Xinghui
  • Bechhoefer, Eric
  • Ding, Siyi

Abstract

The accurate remaining useful life (RUL) prediction is the foundation of prognostics and health management (PHM). The accuracy of RUL prediction model depends on not only the quality and quantity of degradation feature but also the prediction model. In most of the existing deep-learning based RUL prediction models, noise is considered harmful and has to be removed. Further, the correlation among sensory measurements is ignored. However, noise can boost the prediction performance if judiciously used. This paper proposes a new RUL prediction method where noise is intentionally added into a long short-term memory (LSTM) network. Additionally, correlation analysis is conducted among the sensory measurements to construct new degradation features as the inputs of the LSTM network. Validation of the proposed method was carried out on the C-MAPSS aero-engine lifetime dataset. Finally, the proposed RUL prediction model is compared to other the-state-of-the-art techniques.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:215:y:2021:i:c:s0951832021003902
    DOI: 10.1016/j.ress.2021.107871
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832021003902
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2021.107871?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. Shi, Zunya & Chehade, Abdallah, 2021. "A dual-LSTM framework combining change point detection and remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    2. 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.
    3. Minhee Kim & Kaibo Liu, 2020. "A Bayesian deep learning framework for interval estimation of remaining useful life in complex systems by incorporating general degradation characteristics," IISE Transactions, Taylor & Francis Journals, vol. 53(3), pages 326-340, December.
    4. Listou Ellefsen, André & Bjørlykhaug, Emil & Æsøy, Vilmar & Ushakov, Sergey & Zhang, Houxiang, 2019. "Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 240-251.
    5. Xia, Tangbin & Dong, Yifan & Xiao, Lei & Du, Shichang & Pan, Ershun & Xi, Lifeng, 2018. "Recent advances in prognostics and health management for advanced manufacturing paradigms," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 255-268.
    6. Chen, Jinglong & Jing, Hongjie & Chang, Yuanhong & Liu, Qian, 2019. "Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 372-382.
    7. 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).
    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. Zhang, Zhiyao & Chen, Xiaohui & Zio, Enrico & Li, Longxiao, 2023. "Multi-task learning boosted predictions of the remaining useful life of aero-engines under scenarios of working-condition shift," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    2. Vrignat, Pascal & Kratz, Frédéric & Avila, Manuel, 2022. "Sustainable manufacturing, maintenance policies, prognostics and health management: A literature review," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    3. Nguyen, Khanh T.P. & Medjaher, Kamal & Gogu, Christian, 2022. "Probabilistic deep learning methodology for uncertainty quantification of remaining useful lifetime of multi-component systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    4. 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).
    5. 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).
    6. Li, Wanxiang & Shang, Zhiwu & Gao, Maosheng & Qian, Shiqi & Feng, Zehua, 2022. "Remaining useful life prediction based on transfer multi-stage shrinkage attention temporal convolutional network under variable working conditions," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    7. Li, Xilin & Teng, Wei & Peng, Dikang & Ma, Tao & Wu, Xin & Liu, Yibing, 2023. "Feature fusion model based health indicator construction and self-constraint state-space estimator for remaining useful life prediction of bearings in wind turbines," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    8. González-Muñiz, Ana & Díaz, Ignacio & Cuadrado, Abel A. & García-Pérez, Diego, 2022. "Health indicator for machine condition monitoring built in the latent space of a deep autoencoder," Reliability Engineering and System Safety, Elsevier, vol. 224(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. Xiong, Jiawei & Zhou, Jian & Ma, Yizhong & Zhang, Fengxia & Lin, Chenglong, 2023. "Adaptive deep learning-based remaining useful life prediction framework for systems with multiple failure patterns," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    2. Yan, Jianhai & He, Zhen & He, Shuguang, 2023. "Multitask learning of health state assessment and remaining useful life prediction for sensor-equipped machines," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    3. 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).
    4. 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).
    5. Zio, Enrico, 2022. "Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    6. 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).
    7. Bae, Jinwoo & Xi, Zhimin, 2022. "Learning of physical health timestep using the LSTM network for remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    8. Li, Zhanhang & Zhou, Jian & Nassif, Hani & Coit, David & Bae, Jinwoo, 2023. "Fusing physics-inferred information from stochastic model with machine learning approaches for degradation prediction," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    9. Arias Chao, Manuel & Kulkarni, Chetan & Goebel, Kai & Fink, Olga, 2022. "Fusing physics-based and deep learning models for prognostics," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    10. 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).
    11. Fu, Song & Lin, Lin & Wang, Yue & Guo, Feng & Zhao, Minghang & Zhong, Baihong & Zhong, Shisheng, 2024. "MCA-DTCN: A novel dual-task temporal convolutional network with multi-channel attention for first prediction time detection and remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    12. Zhang, Wei & Li, Xiang & Ma, Hui & Luo, Zhong & Li, Xu, 2021. "Transfer learning using deep representation regularization in remaining useful life prediction across operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    13. Wang, Yuan & Lei, Yaguo & Li, Naipeng & Yan, Tao & Si, Xiaosheng, 2023. "Deep multisource parallel bilinear-fusion network for remaining useful life prediction of machinery," Reliability Engineering and System Safety, Elsevier, vol. 231(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. Dehghan Shoorkand, Hassan & Nourelfath, Mustapha & Hajji, Adnène, 2024. "A hybrid CNN-LSTM model for joint optimization of production and imperfect predictive maintenance planning," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    16. Li, Tianfu & Zhao, Zhibin & Sun, Chuang & Yan, Ruqiang & Chen, Xuefeng, 2021. "Hierarchical attention graph convolutional network to fuse multi-sensor signals for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    17. Zhu, Qixiang & Zhou, Zheng & Li, Yasong & Yan, Ruqiang, 2024. "Contrastive BiLSTM-enabled Health Representation Learning for Remaining Useful Life Prediction," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    18. Ding, Yifei & Jia, Minping & Miao, Qiuhua & Huang, Peng, 2021. "Remaining useful life estimation using deep metric transfer learning for kernel regression," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    19. Fu, Song & Zhang, Yongjian & Lin, Lin & Zhao, Minghang & Zhong, Shi-sheng, 2021. "Deep residual LSTM with domain-invariance for remaining useful life prediction across domains," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    20. 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).

    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:215:y:2021:i:c:s0951832021003902. 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.