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A Systematic Semi-Supervised Self-adaptable Fault Diagnostics approach in an evolving environment

Author

Listed:
  • Yang Hu

    (Dipartimento di Energia [Milano] - POLIMI - Politecnico di Milano [Milan])

  • Piero Baraldi

    (Dipartimento di Energia [Milano] - POLIMI - Politecnico di Milano [Milan])

  • Francesco Di Maio

    (Dipartimento di Energia [Milano] - POLIMI - Politecnico di Milano [Milan])

  • Enrico Zio

    (SSEC - Chaire Sciences des Systèmes et Défis Energétiques EDF/ECP/Supélec - Ecole Centrale Paris - Ecole Supérieure d'Electricité - SUPELEC (FRANCE) - CentraleSupélec - EDF R&D - EDF R&D - EDF - EDF, LGI - Laboratoire Génie Industriel - EA 2606 - CentraleSupélec)

Abstract

Fault diagnostic methods are challenged by their applications to industrial components operating in evolving environments of their working conditions. To overcome this problem, we propose a Systematic Semi-Supervised Self-adaptable Fault Diagnostics approach (4SFD), which allows dynamically selecting the features to be used for performing the diagnosis, detecting the necessity of updating the diagnostic model and automatically updating it. Within the proposed approach, the main novelty is the semi-supervised feature selection method developed to dynamically select the set of features in response to the evolving environment. An artificial Gaussian and a real world bearing dataset are considered for the verification of the proposed approach.

Suggested Citation

  • Yang Hu & Piero Baraldi & Francesco Di Maio & Enrico Zio, 2017. "A Systematic Semi-Supervised Self-adaptable Fault Diagnostics approach in an evolving environment," Post-Print hal-01652242, HAL.
    • Handle: RePEc:hal:journl:hal-01652242
    DOI: 10.1016/j.ymssp.2016.11.004
    Note: View the original document on HAL open archive server: https://hal.science/hal-01652242
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    Citations

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    Cited by:

    1. Vincenzo Destino & Nicola Pedroni & Roberto Bonifetto & Francesco Di Maio & Laura Savoldi & Enrico Zio, 2021. "Metamodeling and On-Line Clustering for Loss-of-Flow Accident Precursors Identification in a Superconducting Magnet Cryogenic Cooling Circuit," Energies, MDPI, vol. 14(17), pages 1-37, September.
    2. Puppo, L. & Pedroni, N. & Maio, F. Di & Bersano, A. & Bertani, C. & Zio, E., 2021. "A Framework based on Finite Mixture Models and Adaptive Kriging for Characterizing Non-Smooth and Multimodal Failure Regions in a Nuclear Passive Safety System," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    3. Xiang Li & Xiaodong Jia & Qibo Yang & Jay Lee, 2020. "Quality analysis in metal additive manufacturing with deep learning," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 2003-2017, December.
    4. Yoon, Joung Taek & Youn, Byeng D. & Yoo, Minji & Kim, Yunhan & Kim, Sooho, 2019. "Life-cycle maintenance cost analysis framework considering time-dependent false and missed alarms for fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 181-192.
    5. Hu, Yang & Miao, Xuewen & Si, Yong & Pan, Ershun & Zio, Enrico, 2022. "Prognostics and health management: A review from the perspectives of design, development and decision," Reliability Engineering and System Safety, Elsevier, vol. 217(C).

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