In this paper, random forests are proposed for operating devices diagnostics in the presence of a variable number of features. In various contexts, like large or difficult-to-access monitored areas, wired sensor networks providing features to achieve diagnostics are either very costly to use or totally impossible to spread out. Using a wireless sensor network can solve this problem, but this latter is more subjected to flaws. Furthermore, the networks' topology often changes, leading to a variability in quality of coverage in the targeted area. Diagnostics at the sink level must take into consideration that both the number and the quality of the provided features are not constant, and that some politics like scheduling or data aggregation may be developed across the network. The aim of this article is (1) to show that random forests are relevant in this context, due to their flexibility and robustness, and (2) to provide first examples of use of this method for diagnostics based on data provided by a wireless sensor network.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Random forests for industrial device functioning diagnostics using wireless sensor networks


    Beteiligte:
    Elghazel, Wiem (Autor:in) / Medjaher, Kamal (Autor:in) / Zerhouni, Noureddine (Autor:in) / Bahi, Jacques (Autor:in) / Farhat, Ahmad (Autor:in) / Guyeux, Christophe (Autor:in) / Hakem, Mourad (Autor:in)


    Erscheinungsdatum :

    01.03.2015


    Format / Umfang :

    3962720 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Machine Learning Based Channel Error Diagnostics in Wireless Sensor Networks

    Yi, Su / Wang, Hao / Tian, Jun et al. | IEEE | 2017


    Random Decision Forests

    Ho, T. K. | British Library Conference Proceedings | 1995


    Industrial Wireless Sensor Networks and 5G Connected Industries

    Imran, Muhammad Ali / Abdulrahman Sambo, Yusuf / Abbasi, Qammer H. et al. | Wiley | 2019


    Predicting incident duration using random forests

    Hamad, Khaled / Al-Ruzouq, Rami / Zeiada, Waleed et al. | Taylor & Francis Verlag | 2020


    Travel Time Reliability Prediction Using Random Forests

    Zhao, Mo / Zhang, Xiaoxiao / Appiah, Justice et al. | Transportation Research Record | 2023