IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v253y2025ics0951832024005908.html
   My bibliography  Save this article

A hybrid dual-frequency-informed spider net for RUL prognosis with adaptive IDP detection and outlier correction

Author

Listed:
  • Yang, Qichao
  • Tang, Baoping
  • Deng, Lei
  • Zhang, Xiaolong
  • Wu, Jinzhou

Abstract

The present study proposes a novel framework to estimate the Remaining Useful Life (RUL) of bearings operating under variable operating conditions, addressing two critical challenges: early detection of the Initial Degradation Point (IDP) in bearings and correction of outlier values. A unique Spider cell prediction unit with dual-frequency correction is proposed. Firstly, a generalized adaptive method is introduced for early IDP detection, leveraging the slope and intercept, along with coupled t-tests to formulate a "sum of slopes" index for detecting the IDP. Secondly, a degradation feature extraction method is introduced, which utilizes synchronous pseudo speed in combination with sliding window averaging. Outlier correction for degradation feature indicators is achieved using constructed boundary conditions. Thirdly, a variational mode decomposition layer is proposed to decompose the input sample into different mode function components. Finally, a novel RUL prediction correction module, where two types of frequency domain feature extractors with trainable parameters are designed to adjust the prediction results of the Spider net by capturing both global trend changes and local details.

Suggested Citation

  • Yang, Qichao & Tang, Baoping & Deng, Lei & Zhang, Xiaolong & Wu, Jinzhou, 2025. "A hybrid dual-frequency-informed spider net for RUL prognosis with adaptive IDP detection and outlier correction," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    • Handle: RePEc:eee:reensy:v:253:y:2025:i:c:s0951832024005908
    DOI: 10.1016/j.ress.2024.110518
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832024005908
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2024.110518?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. 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).
    2. 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).
    3. 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).
    4. Cheng, Han & Kong, Xianguang & Wang, Qibin & Ma, Hongbo & Yang, Shengkang, 2022. "The two-stage RUL prediction across operation conditions using deep transfer learning and insufficient degradation data," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    5. Zhang, Shuyi & Zhai, Qingqing & Li, Yaqiu, 2023. "Degradation modeling and RUL prediction with Wiener process considering measurable and unobservable external impacts," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    6. Dong, Shaojiang & Xiao, Jiafeng & Hu, Xiaolin & Fang, Nengwei & Liu, Lanhui & Yao, Jinbao, 2023. "Deep transfer learning based on Bi-LSTM and attention for remaining useful life prediction of rolling bearing," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    7. 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).
    8. Li, Xiang & Zhang, Wei & Ding, Qian, 2019. "Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 208-218.
    9. 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).
    10. 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).
    Full references (including those not matched with items on IDEAS)

    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. Han, Yaoyao & Ding, Xiaoxi & Gu, Fengshou & Chen, Xiaohui & Xu, Minmin, 2025. "Dual-drive RUL prediction of gear transmission systems based on dynamic model and unsupervised domain adaption under zero sample," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    2. Pan, Junlin & Sun, Bo & Wu, Zeyu & Yi, Zechen & Feng, Qiang & Ren, Yi & Wang, Zili, 2024. "Probabilistic remaining useful life prediction without lifetime labels: A Bayesian deep learning and stochastic process fusion method," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    3. Zhu, Rong & Chen, Yuan & Peng, Weiwen & Ye, Zhi-Sheng, 2022. "Bayesian deep-learning for RUL prediction: An active learning perspective," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    4. Wei, Yupeng & Wu, Dazhong & Terpenny, Janis, 2024. "Remaining useful life prediction using graph convolutional attention networks with temporal convolution-aware nested residual connections," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    5. 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).
    6. 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).
    7. Zuo, Tao & Zhang, Kai & Zheng, Qing & Li, Xianxin & Li, Zhixuan & Ding, Guofu & Zhao, Minghang, 2023. "A hybrid attention-based multi-wavelet coefficient fusion method in RUL prognosis of rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    8. Kim, Sunghyun & Seo, Yun-Ho & Park, Junhong, 2024. "Transformer-based novel framework for remaining useful life prediction of lubricant in operational rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    9. Wang, Wei & Song, Honghao & Si, Shubin & Lu, Wenhao & Cai, Zhiqiang, 2024. "Data augmentation based on diffusion probabilistic model for remaining useful life estimation of aero-engines," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    10. Nejjar, Ismail & Geissmann, Fabian & Zhao, Mengjie & Taal, Cees & Fink, Olga, 2024. "Domain adaptation via alignment of operation profile for Remaining Useful Lifetime prediction," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    11. Kamariotis, Antonios & Tatsis, Konstantinos & Chatzi, Eleni & Goebel, Kai & Straub, Daniel, 2024. "A metric for assessing and optimizing data-driven prognostic algorithms for predictive maintenance," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    12. Gao, Pengjie & Wang, Junliang & Shi, Ziqi & Ming, Weiwei & Chen, Ming, 2024. "Long-term temporal attention neural network with adaptive stage division for remaining useful life prediction of rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    13. Bajarunas, Kristupas & Baptista, Marcia L. & Goebel, Kai & Chao, Manuel Arias, 2024. "Health index estimation through integration of general knowledge with unsupervised learning," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    14. Cheng, Han & Kong, Xianguang & Wang, Qibin & Ma, Hongbo & Yang, Shengkang & Xu, Kun, 2023. "Remaining useful life prediction combined dynamic model with transfer learning under insufficient degradation data," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    15. Li, Xuanlin & Hu, Yawei & Wang, Hang & Liu, Yongbin & Liu, Xianzeng & Lu, Huitian, 2025. "A closed-form continuous-depth neural-based hybrid difference features re-representation network for RUL prediction," Reliability Engineering and System Safety, Elsevier, vol. 253(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. Han, Yan & Hu, Ailin & Huang, Qingqing & Zhang, Yan & Lin, Zhichao & Ma, Jinghua, 2025. "Sinkhorn divergence-based contrast domain adaptation for remaining useful life prediction of rolling bearings under multiple operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    18. 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).
    19. Liu, Yang & Zhou, Guangda & Zhao, Shujian & Li, Liang & Xie, Wenhua & Su, Bengan & Li, Yongwei & Zhao, Zhen, 2025. "A novel two-stage method via adversarial strategy for remaining useful life prediction of bearings under variable conditions," Reliability Engineering and System Safety, Elsevier, vol. 254(PA).
    20. 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).

    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:253:y:2025:i:c:s0951832024005908. 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.