IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v30y2019i5d10.1007_s10845-017-1377-4.html
   My bibliography  Save this article

Impacts of wireless sensor networks strategies and topologies on prognostics and health management

Author

Listed:
  • Ahmad Farhat

    (Université de Bourgogne Franche-Comté)

  • Christophe Guyeux

    (Université de Bourgogne Franche-Comté)

  • Abdallah Makhoul

    (Université de Bourgogne Franche-Comté)

  • Ali Jaber

    (Lebanese University)

  • Rami Tawil

    (Lebanese University)

  • Abbas Hijazi

    (Lebanese University)

Abstract

In this article, we used wireless sensor network (WSN) techniques for monitoring an area under consideration, in order to diagnose its state in real time. What differentiates this type of network from the traditional computer ones is that it is composed by a large number of sensor nodes having very limited and almost nonrenewable energy. A key issue in designing such networks is energy conservation because once a sensor depletes its resources, it will be dropped from the network. This will lead to coverage hole and incomplete data arriving to the sink. Therefore, preserving the energy held by the nodes so that the network keeps running for as long as possible is a very important concern. If we achieve to improve the network lifetime and Quality of Service (QoS). Diagnosing the state of area will be more accurate for a longer time. One of the most important elements to achieve a QoS in WSN is the network coverage which is usually interpreted as how well the network can observe a given area. Obviously, if the coverage decreases over time, the diagnosis quality decreases accordingly. Various coverage strategies are thus proposed by the WSN community, in order to guarantee a certain coverage rate as long as possible, to reach a certain QoS that in turn will impact the diagnosis and prognostic quality. Various other strategies are in common use in WSN like data aggregation and scheduling, to preserve a QoS in wireless sensor networks, as long as possible. We argue that such strategies are not neutral if this network is used for prognostic and health management. Some politics may have a positive impact while other ones may blur the sensed data, like data aggregation or redundancy suppression, leading to erroneous diagnostics and/or prognostics. In this work, we will show and measure the impact of each WSN strategy on the resulting estimation of diagnostics. We emphasized several issues and studied various parameters related to these strategies that have a very important impact on the network, and therefore on data diagnostics over time. To reach this goal, to evaluate both prognostic and health management with the WSN strategies, we have used six diagnostic algorithms.

Suggested Citation

  • Ahmad Farhat & Christophe Guyeux & Abdallah Makhoul & Ali Jaber & Rami Tawil & Abbas Hijazi, 2019. "Impacts of wireless sensor networks strategies and topologies on prognostics and health management," Journal of Intelligent Manufacturing, Springer, vol. 30(5), pages 2129-2155, June.
  • Handle: RePEc:spr:joinma:v:30:y:2019:i:5:d:10.1007_s10845-017-1377-4
    DOI: 10.1007/s10845-017-1377-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-017-1377-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10845-017-1377-4?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. Ng, Selina S.Y. & Xing, Yinjiao & Tsui, Kwok L., 2014. "A naive Bayes model for robust remaining useful life prediction of lithium-ion battery," Applied Energy, Elsevier, vol. 118(C), pages 114-123.
    2. 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.
    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. Liming Deng & Wenjing Shen & Kangkang Xu & Xuhui Zhang, 2024. "An Adaptive Modeling Method for the Prognostics of Lithium-Ion Batteries on Capacity Degradation and Regeneration," Energies, MDPI, vol. 17(7), pages 1-15, April.
    2. Cai, Yishan & Yang, Lin & Deng, Zhongwei & Zhao, Xiaowei & Deng, Hao, 2018. "Online identification of lithium-ion battery state-of-health based on fast wavelet transform and cross D-Markov machine," Energy, Elsevier, vol. 147(C), pages 621-635.
    3. Yang, Duo & Wang, Yujie & Pan, Rui & Chen, Ruiyang & Chen, Zonghai, 2018. "State-of-health estimation for the lithium-ion battery based on support vector regression," Applied Energy, Elsevier, vol. 227(C), pages 273-283.
    4. Dawei Song & Shiqian Wang & Li Di & Weijian Zhang & Qian Wang & Jing V. Wang, 2023. "Lithium-Ion Battery Life Prediction Method under Thermal Gradient Conditions," Energies, MDPI, vol. 16(2), pages 1-13, January.
    5. Gu, Xubo & Bai, Hanyu & Cui, Xiaofan & Zhu, Juner & Zhuang, Weichao & Li, Zhaojian & Hu, Xiaosong & Song, Ziyou, 2024. "Challenges and opportunities for second-life batteries: Key technologies and economy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    6. Jun Peng & Zhiyong Zheng & Xiaoyong Zhang & Kunyuan Deng & Kai Gao & Heng Li & Bin Chen & Yingze Yang & Zhiwu Huang, 2020. "A Data-Driven Method with Feature Enhancement and Adaptive Optimization for Lithium-Ion Battery Remaining Useful Life Prediction," Energies, MDPI, vol. 13(3), pages 1-20, February.
    7. Dai, Houde & Wang, Jiaxin & Huang, Yiyang & Lai, Yuan & Zhu, Liqi, 2024. "Lightweight state-of-health estimation of lithium-ion batteries based on statistical feature optimization," Renewable Energy, Elsevier, vol. 222(C).
    8. 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.
    9. 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.
    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. Muhammad Umair Ali & Amad Zafar & Sarvar Hussain Nengroo & Sadam Hussain & Gwan-Soo Park & Hee-Je Kim, 2019. "Online Remaining Useful Life Prediction for Lithium-Ion Batteries Using Partial Discharge Data Features," Energies, MDPI, vol. 12(22), pages 1-14, November.
    12. 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.
    13. 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.
    14. Hu, Chao & Jain, Gaurav & Tamirisa, Prabhakar & Gorka, Tom, 2014. "Method for estimating capacity and predicting remaining useful life of lithium-ion battery," Applied Energy, Elsevier, vol. 126(C), pages 182-189.
    15. Vega, Manuel A. & Hu, Zhen & Todd, Michael D., 2020. "Optimal maintenance decisions for deteriorating quoin blocks in miter gates subject to uncertainty in the condition rating protocol," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    16. Bellaera, R. & Bonifetto, R. & Di Maio, F. & Pedroni, N. & Savoldi, L. & Zanino, R. & Zio, E., 2020. "Integrated deterministic and probabilistic safety assessment of a superconducting magnet cryogenic cooling circuit for nuclear fusion applications," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    17. Chengning Zhang & Xin Jin & Junqiu Li, 2017. "PTC Self-Heating Experiments and Thermal Modeling of Lithium-Ion Battery Pack in Electric Vehicles," Energies, MDPI, vol. 10(4), pages 1-21, April.
    18. Tamilselvan, Prasanna & Wang, Pingfeng, 2013. "Failure diagnosis using deep belief learning based health state classification," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 124-135.
    19. Shuming Wang & Yan-Fu Li & Tong Jia, 2020. "Distributionally Robust Design for Redundancy Allocation," INFORMS Journal on Computing, INFORMS, vol. 32(3), pages 620-640, July.
    20. You, Gae-won & Park, Sangdo & Oh, Dukjin, 2016. "Real-time state-of-health estimation for electric vehicle batteries: A data-driven approach," Applied Energy, Elsevier, vol. 176(C), pages 92-103.

    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:spr:joinma:v:30:y:2019:i:5:d:10.1007_s10845-017-1377-4. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.