IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v261y2025ics0951832025003369.html

Addressing data scarcity in industrial reliability assessment with Physically Informed Echo State Networks

Author

Listed:
  • Sanchez, Luciano
  • Costa, Nahuel
  • Couso, Ines

Abstract

This paper introduces a method for augmenting sensor data using Physically Informed Echo State Networks (ESNs), which facilitates system identification in scenarios with limited sensor data. The approach integrates domain-specific physical knowledge into the learning process of ESNs to generate surrogate time-amplitude signals from the Power Spectral Density (PSD) of the data and a predefined list of system excitation frequencies. This integration proves particularly beneficial during the initial design phases of condition monitoring systems, where empirical data is often sparse. We demonstrate the effectiveness of this method through experiments on a 30 kW jet fan in a road tunnel ventilation system. Results indicate significant improvements in the operational capabilities of condition monitoring systems for newly developed equipment. This method is versatile and applicable across various industrial contexts with insufficient historical operational data.

Suggested Citation

  • Sanchez, Luciano & Costa, Nahuel & Couso, Ines, 2025. "Addressing data scarcity in industrial reliability assessment with Physically Informed Echo State Networks," Reliability Engineering and System Safety, Elsevier, vol. 261(C).
    • Handle: RePEc:eee:reensy:v:261:y:2025:i:c:s0951832025003369
    DOI: 10.1016/j.ress.2025.111135
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832025003369
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2025.111135?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Min Yan & Can Huang & Peter Bienstman & Peter Tino & Wei Lin & Jie Sun, 2024. "Author Correction: Emerging opportunities and challenges for the future of reservoir computing," Nature Communications, Nature, vol. 15(1), pages 1-1, December.
    2. 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).
    3. Xu, Yanwen & Kohtz, Sara & Boakye, Jessica & Gardoni, Paolo & Wang, Pingfeng, 2023. "Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    4. Woo, Junhyuk & Kim, Soon Ho & Kim, Hyeongmo & Han, Kyungreem, 2024. "Characterization of the neuronal and network dynamics of liquid state machines," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    5. Min Yan & Can Huang & Peter Bienstman & Peter Tino & Wei Lin & Jie Sun, 2024. "Emerging opportunities and challenges for the future of reservoir computing," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    6. Rivas, Andy & Delipei, Gregory Kyriakos & Davis, Ian & Bhongale, Satyan & Yang, Jinan & Hou, Jason, 2024. "A component diagnostic and prognostic framework for pump bearings based on deep learning with data augmentation," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    7. Wang, Chenxi & Zhang, Yuxiang & Zhao, Zhibin & Chen, Xuefeng & Hu, Jiawei, 2024. "Dynamic model-assisted transferable network for liquid rocket engine fault diagnosis using limited fault samples," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    8. 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).
    9. Chadha, Mayank & Ramancha, Mukesh K. & Vega, Manuel A. & Conte, Joel P. & Todd, Michael D., 2023. "The modeling of risk perception in the use of structural health monitoring information for optimal maintenance decisions," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    10. 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)

    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, Shi-ao & Wang, Shibin & Wang, Wei & Zheng, Yiming & Ding, Baoqing & Yan, Ruqiang & Chen, Xuefeng, 2025. "Unrolled sparse coding via lifting wavelet for interpretable intelligent fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 264(PB).
    2. Cai, Deyu & Mu, Penghua & Huang, Yu & Zhou, Pei & Li, Nianqiang, 2024. "A reinforced reservoir computer aided by an external asymmetric dual-path-filtering cavity laser," Chaos, Solitons & Fractals, Elsevier, vol. 189(P1).
    3. 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).
    4. Jiang, Chen & Zhong, Teng & Choi, Hyunhee & Youn, Byeng D., 2025. "Physics-informed Gaussian process probabilistic modeling with multi-source data for prognostics of degradation processes," Reliability Engineering and System Safety, Elsevier, vol. 258(C).
    5. Gu, Jiawei & Yuan, Xiangxiang & Li, Xinming & Wang, Yanxue & Xing, Jinduo, 2026. "Symmetric Radial Vectors for uncertainty-aware rotary machinery fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 266(PA).
    6. Lyu, Dongzhen & Niu, Guangxing & Liu, Enhui & Zhang, Bin & Chen, Gang & Yang, Tao & Zio, Enrico, 2022. "Time space modelling for fault diagnosis and prognosis with uncertainty management: A general theoretical formulation," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    7. Li, Lili & Xie, Yiyuan & Jiang, Xiao & Su, Ye & Ye, Yichen & Li, Zelin & Tang, Yuhan, 2025. "Time delay reservoir computing based on mutually coupled add-drop microring resonators," Chaos, Solitons & Fractals, Elsevier, vol. 199(P1).
    8. 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).
    9. Varbella, Anna & Gjorgiev, Blazhe & Sansavini, Giovanni, 2023. "Geometric deep learning for online prediction of cascading failures in power grids," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    10. Yang, Liangli & Pang, Siqing & Zhang, Yutai & Zhou, Yihua & Sun, Xinyue & Kong, Yixiu & Zhang, Yi-Cheng, 2025. "Improved next generation reservoir computing with time decay factor and kernel function," Chaos, Solitons & Fractals, Elsevier, vol. 198(C).
    11. Guo, Chang & Shang, Zuogang & Ren, Jiaxin & Zhao, Zhibin & Ding, Baoqing & Wang, Shibin & Chen, Xuefeng, 2024. "CIS2N: Causal independence and sparse shift network for rotating machinery fault diagnosis in unseen domains," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    12. 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).
    13. 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).
    14. Wang, Zhenya & Zhang, Meng & Chen, Hui & Li, Jinghu & Li, Gaosong & Zhao, Jingshan & Yao, Ligang & Zhang, Jun & Chu, Fulei, 2025. "A generalized fault diagnosis framework for rotating machinery based on phase entropy," Reliability Engineering and System Safety, Elsevier, vol. 256(C).
    15. Barraza, Joaquín Figueroa & Droguett, Enrique López & Martins, Marcelo Ramos, 2024. "SCF-Net: A sparse counterfactual generation network for interpretable fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    16. Wang, Wei & Wang, Zhaoqiang & Cai, Zhiqiang & Hu, Changhua & Si, Shubin, 2025. "Robust uncertainty quantification for online remaining useful life prediction with randomly missing and partially faulty sensor data," Reliability Engineering and System Safety, Elsevier, vol. 262(C).
    17. Gjorgiev, Blazhe & Das, Laya & Merkel, Seline & Rohrer, Martina & Auger, Etienne & Sansavini, Giovanni, 2023. "Simulation-driven deep learning for locating faulty insulators in a power line," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    18. Tian, Zhiyao & Zhou, Shunhua & Yin, Xianfei & Yao, Qiyu & Zhao, Yu, 2026. "A Bayesian pressure‑inversion–driven method for establishing mechanically grounded digital twins of in‑service tunnel linings," Reliability Engineering and System Safety, Elsevier, vol. 265(PB).
    19. Aizpurua, J.I. & Stewart, B.G. & McArthur, S.D.J. & Penalba, M. & Barrenetxea, M. & Muxika, E. & Ringwood, J.V., 2022. "Probabilistic forecasting informed failure prognostics framework for improved RUL prediction under uncertainty: A transformer case study," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    20. 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).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:261:y:2025:i:c:s0951832025003369. 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.